gx优化

chenyc

2025-05-29 92f69c57b920cf62ecc9f15f9ed196fa26dbf2ac

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/*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
*/
 
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`;return m[m.length-1]=" "+m[m.length-1]+"]"+(s?"":d),m}function ch(r){let t=[];for(let e=0;e<r.length;e+=2)t.push([r[e],r[e+1]]);return t}var le=class{constructor(t,e,n){if(this.dtype=e,this.shape=t.slice(),this.size=te(t),n!=null){let o=n.length;_(o===this.size,()=>`Length of values '${o}' does not match the size inferred by the shape '${this.size}'.`)}if(e==="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=n||Yg(e,this.size),this.strides=Di(t)}set(t,...e){e.length===0&&(e=[0]),_(e.length===this.rank,()=>`The number of provided coordinates (${e.length}) must match the rank (${this.rank})`);let n=this.locToIndex(e);this.values[n]=t}get(...t){t.length===0&&(t=[0]);let e=0;for(let o of t){if(o<0||o>=this.shape[e]){let s=`Requested out of range element at ${t}.   Buffer shape=${this.shape}`;throw new Error(s)}e++}let n=t[t.length-1];for(let o=0;o<t.length-1;++o)n+=this.strides[o]*t[o];return this.values[n]}locToIndex(t){if(this.rank===0)return 0;if(this.rank===1)return t[0];let e=t[t.length-1];for(let n=0;n<t.length-1;++n)e+=this.strides[n]*t[n];return e}indexToLoc(t){if(this.rank===0)return[];if(this.rank===1)return[t];let e=new Array(this.shape.length);for(let n=0;n<e.length-1;++n)e[n]=Math.floor(t/this.strides[n]),t-=e[n]*this.strides[n];return e[e.length-1]=t,e}get rank(){return this.shape.length}toTensor(){return ci().makeTensor(this.values,this.shape,this.dtype)}},ci=null,rm=null,dK=null;function q_(r){ci=r}function K_(r){rm=r}function j_(r){dK=r}var Ot=class{constructor(t,e,n,o){this.kept=!1,this.isDisposedInternal=!1,this.shape=t.slice(),this.dtype=e||"float32",this.size=te(t),this.strides=Di(t),this.dataId=n,this.id=o,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let t=await this.data();return rm.buffer(this.shape,this.dtype,t)}bufferSync(){return rm.buffer(this.shape,this.dtype,this.dataSync())}async array(){let t=await this.data();return ac(this.shape,t,this.dtype==="complex64")}arraySync(){return ac(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let t=ci().read(this.dataId);if(this.dtype==="string"){let e=await t;try{return e.map(n=>em(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return t}dataToGPU(t){return this.throwIfDisposed(),ci().readToGPU(this.dataId,t)}dataSync(){this.throwIfDisposed();let t=ci().readSync(this.dataId);if(this.dtype==="string")try{return t.map(e=>em(e))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return t}async bytes(){this.throwIfDisposed();let t=await ci().read(this.dataId);return this.dtype==="string"?t:new Uint8Array(t.buffer)}dispose(){this.isDisposed||(ci().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(t=!1){return rm.print(this,t)}clone(){return this.throwIfDisposed(),rm.clone(this)}toString(t=!1){let e=this.dataSync();return U_(e,this.shape,this.dtype,t)}cast(t){return this.throwIfDisposed(),rm.cast(this,t)}variable(t=!0,e,n){return this.throwIfDisposed(),ci().makeVariable(this,t,e,n)}};Object.defineProperty(Ot,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function O(){return nh("Tensor",()=>Ot)}O();var gl=class extends Ot{constructor(t,e,n,o){super(t.shape,t.dtype,t.dataId,o),this.trainable=e,this.name=n}assign(t){if(t.dtype!==this.dtype)throw new Error(`dtype of the new value (${t.dtype}) and previous value (${this.dtype}) must match`);if(!an(t.shape,this.shape))throw new Error(`shape of the new value (${t.shape}) and previous value (${this.shape}) must match`);ci().disposeTensor(this),this.dataId=t.dataId,ci().incRef(this,null)}dispose(){ci().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(gl,Symbol.hasInstance,{value:r=>r instanceof Ot&&r.assign!=null&&r.assign instanceof Function});var So={};Kt(So,{assertTypesMatch:()=>$0,getTensorsInContainer:()=>ph,isTensorInList:()=>gK,makeTypesMatch:()=>jt});var T0;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(T0||(T0={}));var _0;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(_0||(_0={}));var E0;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(E0||(E0={}));var A0;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(A0||(A0={}));var D0;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(D0||(D0={}));var hK={float32:A0,int32:_0,bool:E0,complex64:D0};function ur(r,t){if(r==="string"||t==="string"){if(r==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${r} with ${t}`)}return hK[r][t]}function xc(r){return ur(r,"int32")}function ox(r){return r!=null&&typeof r=="object"&&"texture"in r&&r.texture instanceof WebGLTexture}function sx(r){return typeof GPUBuffer!="undefined"&&r!=null&&typeof r=="object"&&"buffer"in r&&r.buffer instanceof GPUBuffer}function jt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function $0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function gK(r,t){return t.some(e=>e.id===r.id)}function ph(r){let t=[];return X_(r,t,new Set),t}function X_(r,t,e){if(r==null)return;if(r instanceof Ot){t.push(r);return}if(!xK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),X_(s,t,e))}}function xK(r){return Array.isArray(r)||typeof r=="object"}function R0(r){return r.kernelName!=null}var ix=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},Iu=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ix}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let n=t[e];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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){let{name:t,asyncInit:e}=this.initializeBackendsAndReturnBest();if(e)throw new Error(`The highest priority backend '${t}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(t)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(t){if(!(t in this.registry))if(t in this.registryFactory){let{asyncInit:e}=this.initializeBackend(t);if(e)return null}else return null;return this.registry[t]}findBackendFactory(t){return t in this.registryFactory?this.registryFactory[t].factory:null}registerBackend(t,e,n=1){return t in this.registryFactory?(ta(`${t} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[t]={factory:e,priority:n},!0)}async setBackend(t){if(this.registryFactory[t]==null)throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,this.registry[t]==null){this.backendInstance=null;let{success:e,asyncInit:n}=this.initializeBackend(t);if(!(n?await e:e))return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new rx(this.backendInstance),!0}setupRegisteredKernels(){Jg(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Jg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Uo)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o<this.pendingBackendInitId?!1:(this.registry[t]=i,this.pendingBackendInit=null,!0)).catch(i=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,ta(`Initialization of backend ${t} failed`),ta(i.stack||i.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=n,{success:!0,asyncInit:!1}}catch(n){return ta(`Initialization of backend ${t} failed`),ta(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let n=t[e],{success:o,asyncInit:s}=this.initializeBackend(n);if(s||o)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let n=this.state.tensorInfo.get(e),o=n.backend,s=this.readSync(e),i=o.refCount(e);o.disposeData(e,!0),n.backend=t,t.move(e,s,n.shape,n.dtype,i),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let n=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=t}let o;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return Iu.nextTensorId++}nextVariableId(){return Iu.nextVariableId++}clone(t){let e=T.runKernel(bo,{x:t}),n={x:t},o=i=>({x:()=>{let a="float32",u={x:i},l={dtype:a};return T.runKernel(xo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(ih(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype==="complex64"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=R0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(R0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=ih(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=R0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=b0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=t;n==="string"&&Ho(t[0])&&(s=t.map(u=>wu(u)));let i=o.write(s,e,n),a=new Ot(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n==="string"){let u=this.state.tensorInfo.get(i),l=h0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||"float32";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Ot(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new gl(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*Pp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof gl||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let n=t.size*Pp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=b0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Lp(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=ph(t),n=new Set(e.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let i=this.state.activeScope.track[s];!i.kept&&!n.has(i.id)&&i.dispose()}let o=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],e.forEach(s=>{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof Ot,()=>"The result y returned by f() must be a tensor.");let i=V_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let a={};a[s.id]=n==null?yK(s.shape):n,G_(a,i,l=>this.tidy(l),bK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(Ai(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{_(e.every(a=>a instanceof Ot),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Ot,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),_(Ai(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),_(c.every(m=>m instanceof Ot),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=gc(),n=await this.backend.time(t);return n.wallMs=gc()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ix;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Iu.nextTensorId=0;Iu.nextVariableId=0;function yK(r){let t=eh(te(r),"float32");return T.makeTensor(t,r,"float32")}function F0(){let r=y0();if(r._tfengine==null){let t=new rh(r);r._tfengine=new Iu(t)}return I_(r._tfengine.ENV),q_(()=>r._tfengine),r._tfengine}var T=F0();function bK(r,t){let e={a:r,b:t};return T.runKernel(ao,e)}var Cu={};Kt(Cu,{isBrowser:()=>P0,isMobile:()=>CK,mockIsMobile:()=>IK});function wK(){return typeof navigator!="undefined"&&navigator!=null}var O0;function IK(r){O0=r}function CK(r){if(O0!==void 0)return O0;if(r||wK()){if(r||(r=navigator),r.product==="ReactNative")return!0;let t=r.userAgent||r.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let e=r;return e.userAgentData&&e.userAgentData.mobile}return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function P0(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Nn=L();Nn.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});Nn.registerFlag("IS_BROWSER",()=>P0());Nn.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Nn.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Nn.registerFlag("IS_SAFARI",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Safari/.test(navigator.userAgent)&&/Apple/.test(navigator.vendor));Nn.registerFlag("PROD",()=>!1);Nn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Nn.getBool("DEBUG"));Nn.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Nn.registerFlag("IS_TEST",()=>!1);Nn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>Nn.getBool("DEBUG"));Nn.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);Nn.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU",()=>!1);Nn.registerFlag("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eratedBy,convertedBy:r.convertedBy};if(r.trainingConfig!=null&&(n.trainingConfig=r.trainingConfig),r.weightsManifest!=null){if(!t)throw new Error("modelJSON has weightsManifest but weightSpecs is null");if(!e)throw new Error("modelJSON has weightsManifest but weightData is null");n.weightSpecs=t,n.weightData=e}return r.signature!=null&&(n.signature=r.signature),r.userDefinedMetadata!=null&&(n.userDefinedMetadata=r.userDefinedMetadata),r.modelInitializer!=null&&(n.modelInitializer=r.modelInitializer),r.initializerSignature!=null&&(n.initializerSignature=r.initializerSignature),n}async function nm(r,t){let e,n;return r.weightsManifest!=null&&([e,n]=await t(r.weightsManifest)),B0(r,e,n)}function ea(r){if(r.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:r.modelTopology==null?0:J_(JSON.stringify(r.modelTopology)),weightSpecsBytes:r.weightSpecs==null?0:J_(JSON.stringify(r.weightSpecs)),weightDataBytes:r.weightData==null?0:new vr(r.weightData).byteLength}}function cx(r){let t=[];for(let e of r)t.push(...e.weights);return t}function kK(){let r=e=>{let n=e<<13,o=0;for(;!(n&8388608);)o-=8388608,n<<=1;return n&=-8388609,o+=947912704,n|o},t=new Uint32Array(2048);t[0]=0;for(let e=1;e<1024;e++)t[e]=r(e);for(let e=1024;e<2048;e++)t[e]=939524096+(e-1024<<13);return t}function TK(){let r=new Uint32Array(64);r[0]=0,r[31]=1199570944,r[32]=2147483648,r[63]=3347054592;for(let t=1;t<31;t++)r[t]=t<<23;for(let t=33;t<63;t++)r[t]=2147483648+(t-32<<23);return r}function _K(){let r=new Uint32Array(64);for(let t=0;t<64;t++)r[t]=1024;return r[0]=r[32]=0,r}function EK(){let r=kK(),t=TK(),e=_K();return n=>{let o=new ArrayBuffer(4*n.length),s=new Uint32Array(o);for(let i=0;i<n.length;i++){let a=n[i],u=r[e[a>>10]+(a&1023)]+t[a>>10];s[i]=u}return new Float32Array(o)}}var ve=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return ve.instance==null&&(ve.instance=new ve),ve.instance}static registerSaveRouter(t){ve.getInstance().saveRouters.push(t)}static registerLoadRouter(t){ve.getInstance().loadRouters.push(t)}static getSaveHandlers(t){return ve.getHandlers(t,"save")}static getLoadHandlers(t,e){return ve.getHandlers(t,"load",e)}static getHandlers(t,e,n){let o=[];return(e==="load"?ve.getInstance().loadRouters:ve.getInstance().saveRouters).forEach(i=>{let a=i(t,n);a!==null&&o.push(a)}),o}},nE=r=>ve.registerSaveRouter(r),oE=r=>ve.registerLoadRouter(r),sE=r=>ve.getSaveHandlers(r),iE=(r,t)=>ve.getLoadHandlers(r,t);var V0="tensorflowjs",G0=1,yc="models_store",vu="model_info_store";function aE(){if(!L().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let r=typeof window=="undefined"?self:window,t=r.indexedDB||r.mozIndexedDB||r.webkitIndexedDB||r.msIndexedDB||r.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function W0(r){let t=r.result;t.createObjectStore(yc,{keyPath:"modelPath"}),t.createObjectStore(vu,{keyPath:"modelPath"})}var ra=class{constructor(t){if(this.indexedDB=aE(),t==null||!t)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=t}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,t)}async load(){return this.databaseAction(this.modelPath)}databaseAction(t,e){return new Promise((n,o)=>{let s=this.indexedDB.open(V0,G0);s.onupgradeneeded=()=>W0(s),s.onsuccess=()=>{let i=s.result;if(e==null){let a=i.transaction(yc,"readonly"),l=a.objectStore(yc).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return i.close(),o(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=c=>(i.close(),o(l.error)),a.oncomplete=()=>i.close()}else{e.weightData=vr.join(e.weightData);let a=ea(e),u=i.transaction(vu,"readwrite"),l=u.objectStore(vu),c;try{c=l.put({modelPath:this.modelPath,modelArtifactsInfo:a})}catch(m){return o(m)}let p;c.onsuccess=()=>{p=i.transaction(yc,"readwrite");let m=p.objectStore(yc),f;try{f=m.put({modelPath:this.modelPath,modelArtifacts:e,modelArtifactsInfo:a})}catch(d){return o(d)}f.onsuccess=()=>n({modelArtifactsInfo:a}),f.onerror=d=>{l=u.objectStore(vu);let h=l.delete(this.modelPath);h.onsuccess=()=>(i.close(),o(f.error)),h.onerror=g=>(i.close(),o(f.error))}},c.onerror=m=>(i.close(),o(c.error)),u.oncomplete=()=>{p==null?i.close():p.oncomplete=()=>i.close()}}},s.onerror=i=>o(s.error)})}};ra.URL_SCHEME="indexeddb://";var lE=r=>L().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(ra.URL_SCHEME)?AK(r.slice(ra.URL_SCHEME.length)):null;ve.registerSaveRouter(lE);ve.registerLoadRouter(lE);function AK(r){return new ra(r)}function DK(r){return r.startsWith(ra.URL_SCHEME)?r.slice(ra.UR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turn i==="channelsFirst"?H=[u,q,V,G,W]:i==="channelsLast"&&(H=[u,V,G,W,q]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:m,outDepth:V,outHeight:G,outWidth:W,outChannels:q,padInfo:P,strideDepth:x,strideHeight:b,strideWidth:w,filterDepth:f,filterHeight:d,filterWidth:h,effectiveFilterDepth:A,effectiveFilterHeight:D,effectiveFilterWidth:F,dilationDepth:I,dilationHeight:N,dilationWidth:E,inShape:r,outShape:H,filterShape:t}}function uj(r,t,e,n,o){n==null&&(n=Y0(r,t,e));let s=r[0],i=r[1],a=hh((s-t+2*n)/e+1,o),u=hh((i-t+2*n)/e+1,o);return[a,u]}function cj(r,t,e,n,o,s){o==null&&(o=Y0(r,t[0],n[0]));let i=[0,0,0,e];for(let a=0;a<3;a++)r[a]+2*o>=t[a]&&(i[a]=hh((r[a]-t[a]+2*o)/n[a]+1,s));return i}function Y0(r,t,e,n=1){let o=um(t,n);return Math.floor((r[0]*(e-1)-e+o)/2)}function dh(r){return typeof r=="number"?[r,r,r]:r.length===2?[r[0],r[1],1]:r}function j0(r){return typeof r=="number"?[r,r,r]:r}function um(r,t){return t<=1?r:r+(r-1)*(t-1)}function pj(r,t,e,n,o,s,i,a,u){let l,c,p;if(typeof r=="number"){l={top:r,bottom:r,left:r,right:r,type:r===0?"VALID":"NUMBER"};let f=uj([t,e],s,n,r,a);c=f[0],p=f[1]}else if(r==="same"){c=Math.ceil(t/n),p=Math.ceil(e/o);let m=Math.max(0,(c-1)*n+s-t),f=Math.max(0,(p-1)*o+i-e),d=Math.floor(m/2),h=m-d,g=Math.floor(f/2),x=f-g;l={top:d,bottom:h,left:g,right:x,type:"SAME"}}else if(r==="valid")l={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-s+1)/n),p=Math.ceil((e-i+1)/o);else if(typeof r=="object"){let m=u==="channelsLast"?r[1][0]:r[2][0],f=u==="channelsLast"?r[1][1]:r[2][1],d=u==="channelsLast"?r[2][0]:r[3][0],h=u==="channelsLast"?r[2][1]:r[3][1];l={top:m,bottom:f,left:d,right:h,type:m===0&&f===0&&d===0&&h===0?"VALID":"EXPLICIT"},c=hh((t-s+m+f)/n+1,a),p=hh((e-i+d+h)/o+1,a)}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:l,outHeight:c,outWidth:p}}function mj(r,t,e,n,o,s,i,a,u,l,c){let p,m,f,d;if(r==="valid"&&(r=0),typeof r=="number"){p={top:r,bottom:r,left:r,right:r,front:r,back:r,type:r===0?"VALID":"NUMBER"};let g=cj([t,e,n,1],[a,u,l],1,[o,s,i],r,c);m=g[0],f=g[1],d=g[2]}else if(r==="same"){m=Math.ceil(t/o),f=Math.ceil(e/s),d=Math.ceil(n/i);let h=(m-1)*o+a-t,g=(f-1)*s+u-e,x=(d-1)*i+l-n,b=Math.floor(h/2),w=h-b,I=Math.floor(g/2),N=g-I,E=Math.floor(x/2),A=x-E;p={top:I,bottom:N,left:E,right:A,front:b,back:w,type:"SAME"}}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:p,outDepth:m,outHeight:f,outWidth:d}}function hh(r,t){if(!t)return Math.trunc(r);switch(t){case"round":return Math.round(r);case"ceil":return Math.ceil(r);case"floor":return Math.floor(r);default:throw new Error(`Unknown roundingMode ${t}`)}}function co(r){let[t,e,n]=dh(r);return t===1&&e===1&&n===1}function Rr(r,t){return co(r)||co(t)}function sa(r){return dh(r).every(t=>t>0)}function vE(r){if(r==="NHWC")return"channelsLast";if(r==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${r}`)}function Se(r,t,e){if(e!=null){if(typeof t=="string")throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t}.`);if(typeof t=="number")_($a(t),()=>`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t}.`);else if(typeof t=="object")t.forEach(n=>{n.forEach(o=>{_($a(o),()=>`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${o}.`)})});else throw Error(`Error in ${r}: Unknown padding parameter: ${t}`)}}function fj(r,t){let n={x:C(r,"x","reshape","string_or_numeric")},o={shape:t};return T.runKernel(Ui,n,o)}var R=k({reshape_:fj});function dj(r,t,e,n,o){let s=C(r,"x","avgPool","float32"),i=1;_(Rr(e,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${i}'`);let a=s,u=!1;s.rank===3&&(u=!0,a=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),_(a.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${a.rank}.`),Se("avgPool",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o},p=T.runKernel(ts,l,c);return p=Q(p,s.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Su=k({avgPool_:dj});function hj(r,t,e,n,o,s="NDHWC"){let i=C(r,"x","avgPool3d","float32"),a=i,u=!1;i.rank===4&&(u=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(a.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${a.rank}.`),_(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),_(typeof e=="number"&&e>0||Array.isArray(e)&&e[0]>0&&e[1]>0&&e[2]>0,()=>`Error in avgPool3d: Stride must be > 0, but got '${e}'`),Se("avgPool3d",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o,dataFormat:s},p=T.runKernel(Oi,l,c);return p=Q(p,a.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var vx=k({avgPool3d_:hj});function gj(r,t=0){_(r.length>=1,()=>"Pass at least one tensor to concat");let e=xl(r,"tensors","concat","string_or_numeric");if(e[0].dtype==="complex64"&&e.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${s.dtype}. `)}),e.length===1)return cn(e[0]);let n=e,o={axis:t};return T.runKernel(Mi,n,o)}var ie=k({concat_:gj});function xj(r,t,e=!1,n=!1){let o=C(r,"a","matMul"),s=C(t,"b","matMul");[o,s]=jt(o,s);let i={a:o,b:s},a={transposeA:e,transposeB:n};return T.runKernel(es,i,a)}var Bt=k({matMul_:xj});function yj(r){let e={x:C(r,"x","sigmoid","float32")};return T.runKernel(Qs,e)}var en=k({sigmoid_:yj});function bj(r,t,e){let n=C(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:t,size:e};return T.runKernel(qi,o,s)}var Pt=k({slice_:bj});function wj(r){let e={x:C(r,"x","tanh","float32")};return T.runKernel(ai,e)}var ia=k({tanh_:wj});function Ij(r,t,e,n,o,s){let i=C(r,"forgetBias","basicLSTMCell"),a=C(t,"lstmKernel","basicLSTMCell"),u=C(e,"lstmBias","basicLSTMCell"),l=C(n,"data","basicLSTMCell"),c=C(o,"c","basicLSTMCell"),p=C(s,"h","basicLSTMCell"),m=ie([l,p],1),f=Bt(m,a),d=Y(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Pt(d,[0,0],x),w=Pt(d,[0,g],x),I=Pt(d,[0,g*2],x),N=Pt(d,[0,g*3],x),E=Y($(en(b),ia(w)),$(c,en(Y(i,I)))),A=$(ia(E),en(N));return[E,A]}var SE=k({basicLSTMCell_:Ij});function Cj(r,t,e){let n=C(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length  ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return T.runKernel(Pi,s,i)}var Nu=k({batchToSpaceND_:Cj});function NE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function vj(r,t,e,n,o,s){s==null&&(s=.001);let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;n!=null&&(c=C(n,"offset","batchNorm")),_(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||a.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||a.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:NE(i),scale:l,offset:c,mean:a,variance:u},f={varianceEpsilon:s},d=T.runKernel(ys,m,f);return R(d,i.shape)}var aa=k({batchNorm_:vj});function Sj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),aa(i,a,u,c,l,s)}var Sx=k({batchNorm2d_:Sj});function Nj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),_(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),aa(i,a,u,c,l,s)}var Nx=k({batchNorm3d_:Nj});function kj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),_(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),_(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),aa(i,a,u,c,l,s)}var kx=k({batchNorm4d_:kj});function Tj(r,t,e){let n=C(r,"x","bincount"),o=C(t,"weights","bincount");_(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return T.runKernel(Oa,s,i)}var Tx=k({bincount_:Tj});function _j(r,t){let e=C(r,"x","bitwiseAnd"),n=C(t,"y","bitwiseAnd");if(!an(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!=="int32"||n.dtype!=="int32")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel(Pa,o)}var kE=k({bitwiseAnd_:_j});function Ej(r,t){let e=C(r,"s0","broadcastArgs","int32"),n=C(t,"s1","broadcastArgs","int32");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return T.runKernel(Ql,o)}var TE=k({broadcastArgs_:Ej});function Aj(r,t){let e=C(r,"broadcastTo","x"),n=e.shape;if(Me(t),t.length<e.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${e.rank}.`);if(t.length>e.rank){let l=e.shape.slice();for(;l.length<t.length;)l.unshift(1);e=R(e,l)}let o=e.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return cn(e);let a={x:e},u={reps:s};return T.runKernel(lo,a,u)}var la=k({broadcastTo_:Aj});function Dj(r){let e={x:C(r,"x","ceil","float32")};return T.runKernel(rs,e)}var _x=k({ceil_:Dj});function No(r,t,e){Me(r),e=e||Yl(t);let n={shape:r,value:t,dtype:e};return T.runKernel(su,{},n)}function $j(r,t,e){let n=C(r,"x","clipByValue");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return No(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return T.runKernel(yo,o,s)}var Sr=k({clipByValue_:$j});function Rj(r){return ie(r,0)}var Ex=k({concat1d_:Rj});function Fj(r,t){return ie(r,t)}var Ax=k({concat2d_:Fj});function Oj(r,t){return ie(r,t)}var Dx=k({concat3d_:Oj});function Pj(r,t){return ie(r,t)}var $x=k({concat4d_:Pj});function Mj(r,t,e,n,o="NHWC",s=[1,1],i){let a=C(r,"x","conv2d","float32"),u=C(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),Se("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),_(Rr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),_(sa(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),_(sa(e),()=>"Error in conv2D: Strides should be larger than 0.");let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=T.runKernel(ns,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Tn=k({conv2d_:Mj});function Lj(r,t,e,n,o="NWC",s=1,i){let a=C(r,"x","conv1d"),u=C(t,"filter","conv1d"),l=a,c=!1;a.rank===2&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1]])),_(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),_(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),Se("conv1d",n,i),_(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),_(Rr(e,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`),_(sa(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),_(sa(e),()=>"Error in conv1D: Stride should be larger than 0."),_(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=Tn(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var cm=k({conv1d_:Lj});function zj(r,t,e,n,o,s="NHWC",i){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let a=r,u=t,l=!1;t.rank===3&&(l=!0,u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]),a=[1,r[0],r[1],r[2]]),_(a.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${a.length}.`),_(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),_(e.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`);let c=s==="NHWC"?a[3]:a[1],p=s==="NHWC"?u.shape[3]:u.shape[1];_(c===e.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`),_(p===e.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${e.shape[3]}.`),Se("conv2dDerInput",o,i);let m={dy:u,filter:e},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,inputShape:a},d=T.runKernel(os,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var pm=k({conv2DBackpropInput_:zj});function Bj(r,t,e,n,o,s){let i=C(r,"x","conv2dTranspose"),a=C(t,"filter","conv2dTranspose");return pm(e,i,a,n,o,"NHWC",s)}var mm=k({conv2dTranspose_:Bj});function Vj(r,t,e,n,o="NDHWC",s=[1,1,1]){let i=C(r,"x","conv3d"),a=C(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),_(a.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${a.rank}.`),_(u.shape[4]===a.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${a.shape[3]}.`),_(Rr(e,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),_(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`),_(sa(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),_(sa(e),()=>"Error in conv3D: Strides should be larger than 0.");let c={x:u,filter:a},p={strides:e,pad:n,dataFormat:o,dilations:s},m=T.runKernel(ss,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Rx=k({conv3d_:Vj});function Gj(r,t,e,n,o){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let s=r,i=t,a=!1;t.rank===4&&(a=!0,i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let u=s[4],l=i.shape[4];_(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),_(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),_(e.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${e.rank}`),_(u===e.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${e.shape[3]}.`),_(l===e.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[4]}.`);let c={dy:i,filter:e},p={pad:o,strides:n,inputShape:s},m=T.runKernel(La,c,p);return a?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Fx=k({conv3DBackpropInput_:Gj});function Wj(r,t,e,n,o){let s=C(r,"x","conv3dTranspose"),i=C(t,"filter","conv3dTranspose");return Fx(e,s,i,n,o)}var Ox=k({conv3dTranspose_:Wj});function Uj(r){let e={x:C(r,"x","cos","float32")};return T.runKernel(is,e)}var ku=k({cos_:Uj});function Hj(r){let e={x:C(r,"x","cosh","float32")};return T.runKernel(as,e)}var fm=k({cosh_:Hj});function qj(r,t=0,e=!1,n=!1){let s={x:C(r,"x","cumprod")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(za,s,i)}var Ic=k({cumprod_:qj});function Kj(r,t=0,e=!1,n=!1){let s={x:C(r,"x","cumsum")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(ls,s,i)}var dm=k({cumsum_:Kj});function jj(r,t,e,n=!1){let o=C(r,"x","denseBincount"),s=C(t,"weights","denseBincount");_(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(eu,i,a)}var gh=k({denseBincount_:jj});function Xj(r,t,e="NHWC"){let n=C(r,"x","depthToSpace","float32"),o=e==="NHWC"?n.shape[1]:n.shape[2],s=e==="NHWC"?n.shape[2]:n.shape[3],i=e==="NHWC"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),_(o*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${o} and ${t}  for depthToSpace with input shape
    ${n.shape}`),_(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t} for depthToSpace with input shape
        ${n.shape}`),_(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${n.shape}`);let a={x:n},u={blockSize:t,dataFormat:e};return T.runKernel(Va,a,u)}var Px=k({depthToSpace_:Xj});function Yj(r,t,e,n,o="NHWC",s=[1,1],i){let a=C(r,"x","depthwiseConv2d","float32"),u=C(t,"filter","depthwiseConv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${p}) must match the inChannels dimension in filter ${u.shape[2]}.`),Se("depthwiseConv2d",n,i);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=T.runKernel(us,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var ua=k({depthwiseConv2d_:Yj});function Zj(r){let e={x:C(r,"x","diag")};return T.runKernel(ru,e)}var _E=k({diag_:Zj});function Jj(r,t,e,n,o=[1,1],s="NHWC"){let i=C(r,"x","dilation2d"),a=C(t,"filter","dilation2d");_(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),_(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),_(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0),_(u.shape[3]===a.shape[2],()=>`Error in dilation2d:  input and filter must have the same depth: ${u.shape[3]} vs ${a.shape[2]}`);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=T.runKernel(cs,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Mx=k({dilation2d_:Jj});var Hr={};Kt(Hr,{assertAndGetBroadcastShape:()=>Mt,getBroadcastDims:()=>EE,getReductionAxes:()=>ye});function EE(r,t){let e=r.length,n=[];for(let o=0;o<e;o++){let s=e-1-o,i=r[s]||1;(t[t.length-1-o]||1)>1&&i===1&&n.unshift(s)}return n}function ye(r,t){let e=[];for(let n=0;n<t.length;n++){let o=r[r.length-n-1],s=t.length-n-1,i=t[s];(o==null||o===1&&i>1)&&e.unshift(s)}return e}function Mt(r,t){let e=Math.max(r.length,t.length),n=new Array(e);for(let o=0;o<e;o++){let s=r[r.length-o-1];s==null&&(s=1);let i=t[t.length-o-1];if(i==null&&(i=1),s===1)n[e-o-1]=i;else if(i===1)n[e-o-1]=s;else if(s!==i){let a=`Operands could not be broadcast together with shapes ${r} and ${t}.`;throw Error(a)}else n[e-o-1]=s}return n}function Qj(r,t){let e=C(r,"a","equal","string_or_numeric"),n=C(t,"b","equal","string_or_numeric");[e,n]=jt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Wa,o)}var Fr=k({equal_:Qj});function t6(r,t,e){let n=C(t,"a","where"),o=C(e,"b","where"),s=C(r,"condition","where","bool"),i=Mt(Mt(s.shape,n.shape),o.shape),a=la(s,i),u=la(n,i),l=la(o,i),c={condition:a,t:u,e:l};return T.runKernel(Hi,c)}var be=k({where_:t6});function e6(r){let e={x:C(r,"x","zerosLike")};return T.runKernel(Yi,e)}var vt=k({zerosLike_:e6});function r6(r,t){let e=C(r,"a","div"),n=C(t,"b","div");[e,n]=jt(e,n);let o=ct(e,n),s=vt(o),i=Fr(n,s);return be(i,s,o)}var Lx=k({divNoNan_:r6});function n6(r,t){let e=C(r,"t1","dot"),n=C(t,"t2","dot");_((e.rank===1||e.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(_(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),e.rank===1&&n.rank===1){let i=R(e,[1,-1]),a=R(n,[-1,1]),u=Bt(i,a);return R(u,[])}else if(e.rank===1&&n.rank===2){let i=R(e,[1,-1]),a=R(n,[n.shape[0],n.shape[1]]),u=Bt(i,a);return R(u,[u.size])}else if(e.rank===2&&n.rank===1){let i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var zx=k({dot_:n6});function o6(r,...t){let e=t.map((o,s)=>C(o,`tensors${s}`,"einsum")),n={equation:r};return T.runKernel(Wp,e,n)}var AE=k({einsum_:o6});function s6(r){let e={x:C(r,"x","elu","float32")};return T.runKernel(ms,e)}var ca=k({elu_:s6});function i6(r,t){let e=C(r,"x","ensureShape","string_or_numeric");if(!c0(e.shape,t))throw new Error(`EnsureShape: Shape of tensor ${e.shape} is not compatible with expected shape ${t}`);return r}var DE=k({ensureShape_:i6});function a6(r){let t=C(r,"x","erf");_(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=Q(t,"float32"));let e={x:t};return T.runKernel(fs,e)}var Bx=k({erf_:a6});function Z0(r,t){for(let e=0;e<r.length;++e)if(r[r.length-e-1]!==t-1-e)return!1;return!0}function $E(r,t,e){let n=r.length+t.length,o=[],s=0,i=0;for(let a=0;a<n;a++)e.indexOf(a)===-1?o.push(r[s++]):o.push(t[i++]);return o}function J0(r,t){let e=[],n=r.length;for(let s=0;s<n;s++)t.indexOf(s)===-1&&e.push(r[s]);let o=t.map(s=>r[s]);return[e,o]}function ko(r,t){let e=t.map(n=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e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(ja,o)}var Un=k({lessEqual_:R6});function FE(r,t,e){if(e<=0)throw new Error("The number of values should be positive.");let n={start:r,stop:t,num:e};return T.runKernel(Xa,{},n)}function F6(r,t=5,e=1,n=1,o=.5){let s=C(r,"x","localResponseNormalization");_(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
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    the f you passed encloses all operations that lead from x to y.`)}function V6(r){let e={x:C(r,"x","neg")};return T.runKernel(Vi,e)}var Ut=k({neg_:V6});function G6(r){let e={x:C(r,"x","softplus")};return T.runKernel(ti,e)}var pi=k({softplus_:G6});function W6(r){let t=C(r,"x","logSigmoid");return fn(n=>({value:Ut(pi(Ut(n))),gradFunc:i=>$(i,en(Ut(n)))}))(t)}var Xx=k({logSigmoid_:W6});function U6(r,t){let e=C(r,"a","sub"),n=C(t,"b","sub");[e,n]=jt(e,n);let o={a:e,b:n};return T.runKernel(si,o)}var lt=k({sub_:U6});function H6(r,t=-1){let e=C(r,"logits","logSoftmax");if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${e.rank} and axis was ${t}`);return fn((o,s)=>{let a=Nr(o,t,!0),u=lt(o,a),l=lt(Q(u,"float32"),kr(pt(ir(u),t,!0)));return s([l]),{value:l,gradFunc:(p,m)=>{let[f]=m,d=!0,h=ir(f);return lt(p,$(pt(p,t,d),h))}}})(e)}var hm=k({logSoftmax_:H6});function q6(r,t=null,e=!1){let n=C(r,"x","logSumExp"),o=fr(t,n.shape),s=Nr(n,o,!0),i=lt(n,s),a=ir(i),u=pt(a,o),l=kr(u),c=Y(R(s,l.shape),l);if(e){let p=ko(c.shape,o);return R(c,p)}return c}var gm=k({logSumExp_:q6});function K6(r,t){let e=C(r,"a","logicalAnd","bool"),n=C(t,"b","logicalAnd","bool");Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Ya,o)}var Pr=k({logicalAnd_:K6});function j6(r){let e={x:C(r,"x","logicalNot","bool")};return T.runKernel(Za,e)}var Au=k({logicalNot_:j6});function X6(r,t){let e=C(r,"a","logicalOr","bool"),n=C(t,"b","logicalOr","bool");Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Ja,o)}var xm=k({logicalOr_:X6});function Y6(r,t){let e=C(r,"a","logicalXor","bool"),n=C(t,"b","logicalXor","bool");return Mt(e.shape,n.shape),Pr(xm(r,t),Au(Pr(r,t)))}var Yx=k({logicalXor_:Y6});var Zx=2147483648;function Z6(r,t,e="left"){let n=C(r,"sortedSequence","searchSorted"),o=C(t,"values","searchSorted"),s=n.shape[n.shape.length-1],i=o.shape[o.shape.length-1],a=R(n,[-1,s]),u=R(o,[-1,i]);if(a.rank<2)throw new Error("Sorted input argument must be at least 2-dimensional");if(a.shape[0]!==u.shape[0])throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");if(te(u.shape)>=Zx)throw new Error(`values tensor size must less than ${Zx}`);if(a.shape[1]>=Zx)throw new Error(`trailing dim_size must less than ${Zx} for int32 output type, was ${a.shape[1]}`);let l={sortedSequence:a,values:u},c={side:e};return T.runKernel(ul,l,c)}var yh=k({searchSorted_:Z6});function OE(r,t){return yh(r,t,"left")}function J6(r,t,e,n,o){let s=C(r,"x","maxPool"),i=1,a=s,u=!1;s.rank===3&&(u=!0,a=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),_(a.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.rank}.`),_(Rr(e,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${i}'`),Se("maxPool",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o},p=T.runKernel(Es,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Du=k({maxPool_:J6});function Q6(r,t=[1,1,1],e,n,o,s="NDHWC"){let i=C(r,"x","maxPool3d"),a=i,u=!1;i.rank===4&&(u=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(a.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${a.rank}.`),_(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Se("maxPool3d",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o,dataFormat:s},p=T.runKernel(Bi,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Jx=k({maxPool3d_:Q6});function tX(r,t,e,n,o=!1){let i={x:C(r,"x","maxPoolWithArgmax")},a={filterSize:t,strides:e,pad:n,includeBatchInIndex:o},u=T.runKernel(lu,i,a);return{result:u[0],indexes:u[1]}}var PE=k({maxPoolWithArgmax_:tX});function eX(r,t){let e=C(r,"a","maximum"),n=C(t,"b","maximum");[e,n]=jt(e,n),e.dtype==="bool"&&(e=Q(e,"int32"),n=Q(n,"int32")),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(_s,o)}var _n=k({maximum_:eX});function rX(r,t=null,e=!1){let o={x:C(r,"x","mean")},s={axis:t,keepDims:e};return T.runKernel(As,o,s)}var ke=k({mean_:rX});function Te(r,t="float32"){if(Me(r),t==="complex64"){let n=Te(r,"float32"),o=Te(r,"float32");return kn(n,o)}let e=Lp(te(r),t);return T.makeTensor(e,r,t)}function dr(r,t="float32"){if(Me(r),t==="complex64"){let n=dr(r,"float32"),o=Te(r,"float32");return kn(n,o)}let e=eh(te(r),t);return T.makeTensor(e,r,t)}function ME(r,t,{indexing:e="xy"}={}){if(e!=="xy"&&e!=="ij")throw new TypeError(`${e} is not a valid third argument to meshgrid`);if(r===void 0)return[];let n=C(r,"x","meshgrid",r instanceof Ot?r.dtype:"float32");if(t===void 0)return[n];let o=C(t,"y","meshgrid",t instanceof Ot?t.dtype:"float32"),s=te(n.shape),i=te(o.shape);return e==="xy"?(n=R(n,[1,-1]),o=R(o,[-1,1]),[Bt(dr([i,1],n.dtype),n),Bt(o,dr([1,s],o.dtype))]):(n=R(n,[-1,1]),o=R(o,[1,-1]),[Bt(n,dr([1,i],n.dtype)),Bt(dr([s,1],o.dtype),o)])}function nX(r,t){let e=C(r,"a","minimum"),n=C(t,"b","minimum");[e,n]=jt(e,n),e.dtype==="bool"&&(e=Q(e,"int32"),n=Q(n,"int32"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Actual:   ${o}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let a=o[i],u=s[i];if(!e(a,u))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${u}.
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s,n,o)}var hr=k({reverse_:ZX});function JX(r){let t=C(r,"x","reverse");return _(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),hr(t,0)}var yA=k({reverse1d_:JX});function QX(r,t){let e=C(r,"x","reverse");return _(e.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${e.rank}.`),hr(e,t)}var bA=k({reverse2d_:QX});function t5(r,t){let e=C(r,"x","reverse");return _(e.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${e.rank}.`),hr(e,t)}var wA=k({reverse3d_:t5});function e5(r,t){let e=C(r,"x","reverse");return _(e.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${e.rank}.`),hr(e,t)}var IA=k({reverse4d_:e5});function r5(r){let e={x:C(r,"x","round")};return T.runKernel(Ks,e)}var bm=k({round_:r5});function n5(r){let e={x:C(r,"x","rsqrt","float32")};return T.runKernel(js,e)}var wm=k({rsqrt_:n5});function o5(r){let e={x:C(r,"x","selu")};return T.runKernel(Xs,e)}var Im=k({selu_:o5});function s5(r,t,e,n,o,s=[1,1],i="NHWC"){let a=C(r,"x","separableConv2d"),u=C(t,"depthwiseFilter","separableConv2d"),l=C(e,"pointwiseFilter","separableConv2d"),c=a,p=!1;if(a.rank===3&&(p=!0,c=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");_(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),_(u.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`),_(l.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`),_(l.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`),_(l.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);let m=u.shape[2],f=u.shape[3];_(l.shape[2]===m*f,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${m*f}, but got ${l.shape[2]}.`);let d=ua(c,u,n,o,i,s),g=Tn(d,l,1,"valid",i);return p?R(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var Cm=k({separableConv2d_:s5});async function i5(r,t){let e=C(r,"x","setdiff1d"),n=C(t,"y","setdiff1d");_(e.dtype===n.dtype,()=>`x and y should have the same dtype, but got x (${e.dtype}) and y (${n.dtype}).`),_(e.rank===1,()=>`x should be 1D tensor, but got x (${e.shape}).`),_(n.rank===1,()=>`y should be 1D tensor, but got y (${n.shape}).`);let o=await e.data(),s=await n.data(),i=new Set(s),a=0;for(let c=0;c<o.length;c++)i.has(o[c])||a++;let u=new le([a],e.dtype),l=new le([a],"int32");for(let c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return[u.toTensor(),l.toTensor()]}var CA=i5;function a5(r){let e={x:C(r,"x","sign")};return T.runKernel(Js,e)}var uy=k({sign_:a5});function l5(r){let e={x:C(r,"x","sin","float32")};return T.runKernel(Ys,e)}var vm=k({sin_:l5});function u5(r){let e={x:C(r,"x","sinh")};return T.runKernel(Zs,e)}var Sm=k({sinh_:u5});function c5(r,t,e){let n=C(r,"x","slice1d");return _(n.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${n.rank} tensor`),Pt(n,[t],[e])}var Nm=k({slice1d_:c5});function p5(r,t,e){let n=C(r,"x","slice2d");return _(n.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${n.rank} tensor`),Pt(n,t,e)}var wh=k({slice2d_:p5});function m5(r,t,e){let n=C(r,"x","slice3d");return _(n.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${n.rank} tensor`),Pt(n,t,e)}var km=k({slice3d_:m5});function f5(r,t,e){let n=C(r,"x","slice4d");return _(n.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${n.rank} tensor`),Pt(n,t,e)}var Tc=k({slice4d_:f5});function d5(r,t=-1){let e=C(r,"logits","softmax","float32");if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${e.rank} and dim was ${t}`);let n={logits:e},o={dim:t};return T.runKernel(ni,n,o)}var Fu=k({softmax_:d5});function h5(r){_(r.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);let t={input:r};return T.runKernel(Up,t)}var Ou=k({fft_:h5});function g5(r){_(r.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);let t={input:r};return T.runKernel(Hp,t)}var vl=k({ifft_:g5});function x5(r){let t=r.shape[r.shape.length-1],e=r.size/t,n;if(t<=2){let o=R(r,[e,t]);n=vl(o)}else{let o=[e,2*(t-1)],s=R(Cl(r),[e,t]),i=R(Tu(r),[e,t]),a=hr(Pt(s,[0,1],[e,t-2]),1),u=$(hr(Pt(i,[0,1],[e,t-2]),1),ft(-1)),l=ie([s,a],1),c=ie([i,u],1),p=R(kn(l,c),[o[0],o[1]]);n=vl(p)}if(n=Cl(n),r.rank===3&&r.shape[0]!==0){let o=n,s=r.shape[0];n=R(n,[s,n.shape[0]/s,n.shape[1]]),o.dispose()}return n}var Tm=k({irfft_:x5});function y5(r,t,e=0){let o={x:C(r,"x","split")},s={numOrSizeSplits:t,axis:e};return T.runKernel(ji,o,s)}var gr=k({split_:y5});function b5(r,t){_(r.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${r.dtype}`);let e=r.shape[r.shape.length-1],n=r.size/e,o;if(t!=null&&t<e){let d=r.shape.map(g=>0),h=r.shape.map(g=>g);h[r.shape.length-1]=t,o=Pt(r,d,h),e=t}else if(t!=null&&t>e){let d=r.shape.map(h=>h);d[r.shape.length-1]=t-e,o=ie([r,Te(d)],r.shape.length-1),e=t}else o=r;let s=vt(o),i=R(kn(o,s),[n,e]),a=Ou(i),u=Math.floor(e/2)+1,l=Cl(a),c=Tu(a),p=gr(l,[u,e-u],l.shape.length-1),m=gr(c,[u,e-u],c.shape.length-1),f=o.shape.slice();return f[o.shape.length-1]=u,R(kn(p[0],m[0]),f)}var Pu=k({rfft_:b5});function w5(r,t){let e=C(r,"a","squaredDifference"),n=C(t,"b","squaredDifference");[e,n]=jt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n},s={};return T.runKernel(oi,o,s)}var _m=k({squaredDifference_:w5});function I5(r,t){let e=C(r,"x","squeeze","string_or_numeric");return R(e,p0(e.shape,t).newShape)}var qn=k({squeeze_:I5});function C5(r,t=0){let e=xl(r,"tensors","stack","string_or_numeric");_(e.length>=1,()=>"Pass at least one tensor to tf.stack"),e.length>0&&_(t<=e[0].rank,()=>"Axis must be <= rank of the tensor");let n=e,o={axis:t};return T.runKernel(Wi,n,o)}var qe=k({stack_:C5});function v5(r,t=0){let n={x:C(r,"x","step")},o={alpha:t};return T.runKernel(wo,n,o)}var To=k({step_:v5});function S5(r,t,e,n,o=0,s=0,i=0,a=0,u=0){let c={x:C(r,"x","stridedSlice","string_or_numeric")},p={begin:t,end:e,strides:n,beginMask:o,endMask:s,ellipsisMask:i,newAxisMask:a,shrinkAxisMask:u};return T.runKernel(ml,c,p)}var cy=k({stridedSlice_:S5});function N5(r){let e={x:C(r,"x","tan","float32")};return T.runKernel(ii,e)}var py=k({tan_:N5});function Ke(r,t){io(r);let e=Ur(r,t);if(e.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return un(r,null,e,t)}function fi(r,t,e){if(io(r),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let n=Ur(r,e);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 un(r,t,n,e)}function my(r,t,e){if(io(r),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let n=Ur(r,e);if(n.length!==3&&n.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return un(r,t,n,e)}function vA(r,t,e){if(io(r),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let n=Ur(r,e);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 un(r,t,n,e)}function SA(r,t,e){if(io(r),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let n=Ur(r,e);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 flat array");return un(r,t,n,e)}function NA(r,t,e){if(io(r),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let n=Ur(r,e);if(n.length!==6&&n.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||n,un(r,t,n,e)}var Mu={};Kt(Mu,{calculateShapes:()=>kA,validateInput:()=>Em,validateUpdateShape:()=>uN});function uN(r,t,e){let n=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${e.shape}, indices.shape: ${t.shape}, shape: ${r}, sliceDim: ${n}, and batchDim: ${o}.`;if(e.rank<o)throw new Error(s+` update.rank < ${o}. `);if(r.length<n+(e.rank-o))throw new Error(s+` Output shape length < ${n+(e.rank-o)}`);if(e.rank!==o+r.length-n)throw new Error(s+` update.rank != ${o+r.length-n}`);for(let i=0;i<o;++i)if(e.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${e.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<e.rank-o;++i)if(e.shape[i+o]!==r[i+n])throw new Error(s+` updates.shape[${i+o}] (${e.shape[i+o]}) != shape[${i+o}] (${r[i+o]})`)}function Em(r,t,e){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(r.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(e.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${e}`);if(e.length===0){if(t.size===0)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(r.size===0)throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`)}uN(e,t,r)}function kA(r,t,e){let n=t.shape.length,o=n>1?t.shape[n-1]:1,s=e.length,i=1;for(let p=o;p<s;++p)i*=e[p];let a=o<1?1:o,u=te(t.shape)/a,l=[...Di(e.slice(0,o)),1],c=te(e);return{sliceRank:o,numUpdates:u,sliceSize:i,strides:l,outputSize:c}}function k5(r,t,e){let n=C(r,"tensor","tensorScatterupdate"),o=C(t,"indices","tensorScatterupdate","int32"),s=C(e,"updates","tensorScatterupdate");if(Em(s,o,n.shape),n.dtype!==s.dtype)throw new Error(`tensor and updates must have the same dtype, instead they are ${n.dtype} and ${s.dtype}.`);let i={tensor:n,indices:o,updates:s},a={};return T.runKernel(ll,i,a)}var TA=k({tensorScatterUpdate_:k5});function T5(r,t=1,e=!0){let n=C(r,"x","topk");if(n.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let o=n.shape[n.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>o)throw new Error(`'k' passed to topk() must be <= the last dimension (${o}) but got ${t}`);let s={x:n},i={k:t,sorted:e},[a,u]=T.runKernel(fl,s,i);return{values:a,indices:u}}var fy=k({topk_:T5});function _5(r,t=0,e=1,n,o){if(Me(r),n!=null&&n==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new Nc(t,e,n,!0,o),i=wt(r,n);for(let a=0;a<i.values.length;a++)i.values[a]=s.nextValue();return i.toTensor()}var Am=k({truncatedNormal_:_5});function E5(r,t=0){let e=C(r,"x","unique","string_or_numeric");_(e.rank>0,()=>"The input tensor must be at least 1D");let n={x:e},o={axis:t},[s,i]=T.runKernel(xu,n,o);return{values:s,indices:i}}var dy=k({unique_:E5});function A5(r,t,e){let n=C(r,"x","unsortedSegmentSum"),o=C(t,"segmentIds","unsortedSegmentSum","int32");_($a(e),()=>"numSegments must be of dtype int");let s={x:n,segmentIds:o},i={numSegments:e};return T.runKernel(yu,s,i)}var Dm=k({unsortedSegmentSum_:A5});function D5(r,t=0){let e=C(r,"x","unstack","string_or_numeric");_(t>=-e.shape.length&&t<e.shape.length,()=>`Axis = ${t} is not in [-${e.shape.length}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(Xi,n,o)}var xr=k({unstack_:D5});function _A(r,t){return yh(r,t,"right")}function hy(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function gy(r,t){let e=[];for(let s=0;s<t.length;s++)t[s]&&e.push(s);let n=wt(r,"int32"),o=wt([e.length,r.length],"int32");for(let s=0;s<e.length;s++){let i=n.indexToLoc(e[s]),a=s*r.length;o.values.set(i,a)}return o.toTensor()}async function $5(r){let t=C(r,"condition","whereAsync","bool"),e=await t.data(),n=gy(t.shape,e);return r!==t&&t.dispose(),n}var xy=$5;async function R5(r,t,e){let n=C(r,"tensor","boolMask"),o=C(t,"mask","boolMask","bool"),s=e==null?0:e,i=o.rank,a=n.shape;_(i>0,()=>"mask cannot be scalar"),Re(a.slice(s,s+i),o.shape,"mask's shape must match the first K dimensions of tensor's shape,");let u=1;for(let h=s;h<s+i;h++)u*=a[h];let l=a.slice(0,s).concat([u],a.slice(s+i)),c=R(n,l),p=R(o,[-1]),m=await xy(p),f=qn(m,[1]),d=ma(c,f,s);return r!==n&&n.dispose(),t!==o&&o.dispose(),f.dispose(),c.dispose(),p.dispose(),m.dispose(),d}var F5=R5;function O5(r,t,e){let n=C(r,"x","transpose");if(t==null&&(t=n.shape.map((i,a)=>a).reverse()),_(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(i=>{_(i>=0&&i<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype==="complex64"?B(()=>{let i=Cl(n),a=Tu(n);return i=T.runKernel(uo,{x:i},s),a=T.runKernel(uo,{x:a},s),e&&(a=Ut(a)),kn(i,a)}):T.runKernel(uo,o,s)}var Vt=k({transpose_:O5});function P5(r,t,e,n,o=!0){let s=C(r,"v","movingAverage"),i=C(t,"x","movingAverage"),a=C(e,"decay","movingAverage");$0(s,i),_(an(s.shape,i.shape),()=>"Shape mismatch in v and x");let u=ft(1),l=lt(u,a),c=$(lt(i,s),l);if(o){_(n!=null,()=>"When using zeroDebias: true, step is required.");let p=C(n,"step","movingAverage");c=ct(c,lt(u,pn(a,p)))}return Y(s,c)}var M5=k({movingAverage_:P5});function L5(r,t,e){Me(e);let n=C(r,"indices","scatterND","int32"),o=C(t,"updates","scatterND");Em(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(al,s,i)}var z5=k({scatterND_:L5});function EA(r,t,e,n){if(r.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);if(r.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);let o=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(e.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${e.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===o))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${o}]`);if(t.dtype!==n.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function V5(r,t,e,n=0){Me(e);let o=C(r,"sparseIndices","sparseToDense","int32"),s=C(t,"sparseValues","sparseToDense","string_or_numeric"),i=C(n,"defaultValue","sparseToDense",s.dtype);EA(o,s,e,i);let a={sparseIndices:o,sparseValues:s,defaultValue:i},u={outputShape:e};return T.runKernel(pl,a,u)}var G5=k({sparseToDense_:V5});function W5(r,t){let e=C(t,"indices","gatherND","int32"),o={params:C(r,"x","gatherND","string_or_numeric"),indices:e};return T.runKernel(Ha,o)}var U5=k({gatherND_:W5});function AA(r,t){if(t==null)return r.shape.slice();if(an(r.shape,t))return t;if(r.shape.length===t.length){let e=[];for(let n=0;n<r.shape.length;n++)t[n]==null&&r.shape[n]!=null?e.push(r.shape[n]):e.push(t[n]);return e}return t}function H5(r,t,e,n){let o=C(r,"x","dropout");if(_(o.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${o.dtype} tensor instead.`),_(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return r instanceof Ot?o.clone():o;let s=AA(o,e),i=1-t,a=ct(pa(Y(Hn(s,0,1,"float32",n),i)),i);return $(o,a)}var cN=k({dropout_:H5});function pN(r){return Math.floor(Math.pow(2,Math.ceil(Math.log(r)/Math.log(2))))}function Ih(r,t,e){let n=1-r%2,o=new Float32Array(r);for(let s=0;s<r;++s){let i=2*Math.PI*s/(r+n-1);o[s]=t-e*Math.cos(i)}return Ke(o,"float32")}async function q5(r,t,e=1){let n=C(r,"predictions","inTopK"),o=C(t,"targets","inTopK");_(n.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${n.rank}`),_(n.rank-1===o.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${n.rank} and targets rank ${o.rank}`),Re(n.shape.slice(0,n.shape.length-1),o.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=n.shape[n.shape.length-1];_(e>0&&e<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${e}`);let i=await n.data(),a=await o.data(),[u,l]=[i.length/s,s],c=m0("bool",u);for(let p=0;p<u;p++){let m=p*l,f=i.subarray(m,m+l),d=[];for(let h=0;h<f.length;h++)d.push({value:f[h],index:h});d.sort((h,g)=>g.value-h.value),c[p]=0;for(let h=0;h<e;h++)if(d[h].index===a[p]){c[p]=1;break}}return r!==n&&n.dispose(),t!==o&&o.dispose(),sr(c,o.shape,"bool")}var K5=q5;var Lu={};Kt(Lu,{conv2d:()=>DA,depthwiseConv2d:()=>$A,matMul:()=>RA});function j5(r,t,e,n,o,s="NHWC",i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]])),_(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),_(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),_(e.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`);let l=s==="NHWC"?a.shape[3]:a.shape[1],c=s==="NHWC"?u.shape[3]:u.shape[1];_(l===e[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${e[2]}.`),_(c===e[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`),Se("conv2dDerFilter",o,i);let p={x:a,dy:u},m={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,filterShape:e};return T.runKernel(Bp,p,m)}var $m=k({conv2DBackpropFilter_:j5});function _c(r,t,e){if(e==null||e==="linear")return r;if(e==="relu")return $(r,To(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function Ec(r,t){let e=t,n=ye(r.shape,t.shape);return n.length>0&&(e=pt(e,n)),R(e,r.shape)}function Ac(r,t,e,n){if(t==="linear")return r;if(t==="relu")return Mr(r);if(t==="elu")return ca(r);if(t==="relu6")return ym(r);if(t==="prelu")return Ru(r,e);if(t==="leakyrelu")return _u(r,n);if(t==="sigmoid")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Dc=(r,t)=>!(r>0)||t==="linear";function X5({x:r,filter:t,strides:e,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u="linear",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||"linear",Dc(T.state.gradientDepth,u)===!1){_(o==="NHWC",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let E=Tn(r,t,e,n,o,s,i);return a!=null&&(E=Y(E,a)),Ac(E,u,l,c)}let p=C(r,"x","conv2d","float32"),m=C(t,"filter","conv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Se("fused conv2d",n,i);let h=o==="NHWC"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_(Rr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`);let g=wc(f.shape,m.shape,e,s,n,i),x;a!=null&&(x=C(a,"bias","fused conv2d"),[x]=jt(x,p),o==="NHWC"?Mt(g.outShape,x.shape):(_(x.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`),_(x.shape.length===0||x.shape[0]===g.outChannels||x.shape[0]===1,()=>`Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(l!=null){let E=l.shape;if(_(E.length<=1||E.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${E.length}.`),E.length===1)_(E[0]===1||E[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the number of output channels (${g.outChannels}).`);else if(E.length===3)try{Mt(E,g.outShape)}catch(A){let D=`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(D)}b=C(l,"prelu weights","fused conv2d")}let w=(E,A)=>{_(o==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${o} but only NHWC is currently supported.`);let[D,F,P,V]=A,G=_c(E,P,u);_(co(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=pm(F.shape,G,D,e,n),q=$m(F,G,D.shape,e,n),H=[W,q];if(V!=null){let K=Ec(V,G);H.push(K)}return H},I={x:f,filter:m,bias:x,preluActivationWeights:b},N={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?fn((A,D,F)=>{let P=T.runKernel(Ji,I,N);return F([D,A,P]),d&&(P=R(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:w}})(f,m):fn((A,D,F,P)=>{let V=T.runKernel(Ji,I,N);return P([D,A,V,F]),d&&(V=R(V,[V.shape[1],V.shape[2],V.shape[3]])),{value:V,gradFunc:w}})(f,m,x)}var DA=k({fusedConv2d_:X5});function Y5(r,t,e,n,o,s=[1,1],i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={x:a,dy:u},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,filterShape:e};return T.runKernel(Vp,l,c)}var yy=k({depthwiseConv2dNativeBackpropFilter_:Y5});function Z5(r,t,e,n,o,s=[1,1],i){let a=t,u=!1;t.rank===3&&(u=!0,a=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={dy:a,filter:e},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,inputShape:r},p=T.runKernel(Gp,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var by=k({depthwiseConv2dNativeBackpropInput_:Z5});function J5({x:r,filter:t,strides:e,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u="linear",preluActivationWeights:l,leakyreluAlpha:c}){if(Dc(T.state.gradientDepth,u)===!1){let N=ua(r,t,e,n,o,s,i);return a!=null&&(N=Y(N,a)),Ac(N,u,l,c)}let p=C(r,"x","depthwiseConv2d","float32"),m=C(t,"filter","depthwiseConv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`),_(f.shape[3]===m.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`),s==null&&(s=[1,1]),_(Rr(e,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),Se("fused depthwiseConv2d",n,i);let h=wc(f.shape,m.shape,e,s,n,i,!0),g;a!=null&&(g=C(a,"bias","fused conv2d"),[g]=jt(g,p),Mt(h.outShape,g.shape));let x;l!=null&&(x=C(l,"prelu weights","fused depthwiseConv2d"));let b=(N,E)=>{_(co(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[A,D,F,P]=E,V=_c(N,F,u),G=by(D.shape,V,A,e,n,s,i),W=yy(D,V,A.shape,e,n,s,i);if(P!=null){let q=Ec(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?fn((E,A,D)=>{let F=T.runKernel(Qi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):fn((E,A,D,F)=>{let P=T.runKernel(Qi,w,I);return F([A,E,P,D]),d&&(P=R(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:b}})(f,m,g)}var $A=k({fusedDepthwiseConv2d_:J5});function Q5({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Dc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=Y(V,o)),Ac(V,s,i,a)}let u=C(r,"a","fused matMul"),l=C(t,"b","fused matMul");[u,l]=jt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=te(d),x=te(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,"bias","fused matMul"),[E]=jt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,"prelu weights","fused matMul"));let D=(V,G)=>{let[W,q,H,K]=G,X=_c(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(X,q,!1,!0),et=Bt(W,X,!0,!1)):!e&&n?(Z=Bt(X,q,!1,!1),et=Bt(X,W,!0,!1)):e&&!n?(Z=Bt(q,X,!1,!0),et=Bt(W,X,!1,!1)):(Z=Bt(q,X,!0,!0),et=Bt(X,W,!0,!0)),o!=null){let nt=Ec(K,X);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},P={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?fn((G,W,q)=>{let H=T.runKernel(Zi,F,P);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):fn((G,W,q,H)=>{let K=T.runKernel(Zi,F,P);return H([G,W,K,q]),{value:R(K,w),gradFunc:D}})(I,N,E)}var RA=k({fusedMatMul_:Q5});function t8(r){return Ih(r,.54,.46)}var FA=k({hammingWindow_:t8});function e8(r){return Ih(r,.5,.5)}var wy=k({hannWindow_:e8});function r8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Pt(r,s,t)),s+=e;if(n)for(;s<r.size;){let a=s+t-r.size,u=ie([Pt(r,s,t-a),No([a],o)]);i.push(u),s+=e}return i.length===0?fi([],[0,t]):R(ie(i),[i.length,t])}var Iy=k({frame_:r8});function n8(r,t,e,n,o=wy){n==null&&(n=pN(t));let s=Iy(r,t,e),i=$(s,o(t));return Pu(i,n)}var OA=k({stft_:n8});function o8(r,t,e,n,o="bilinear",s=0){let i=C(r,"image","cropAndResize"),a=C(t,"boxes","cropAndResize","float32"),u=C(e,"boxInd","cropAndResize","int32"),l=a.shape[0];_(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o==="bilinear"||o==="nearest",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ba,c,p)}var PA=k({cropAndResize_:o8});function s8(r){let t=C(r,"image","flipLeftRight","float32");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ua,e,{})}var MA=k({flipLeftRight_:s8});function i8(r){let t=C(r,"image","grayscaleToRGB"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Or(t,o)}var LA=k({grayscaleToRGB_:i8});function a8(r,t,e=0,n=.5){let o=C(r,"image","rotateWithOffset","float32");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(hl,s,i)}var zA=k({rotateWithOffset_:a8});function _o(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>"scores must be a 1D tensor"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),_(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s}}function l8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,"boxes","nonMaxSuppression","float32"),i=C(t,"scores","nonMaxSuppression","float32"),a=_o(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u={maxOutputSize:e,iouThreshold:n,scoreThreshold:o};return T.runKernel(rl,{boxes:s,scores:i},u)}var BA=k({nonMaxSuppression_:l8});function VA(r,t,e){let n=u8(r,t,e),o=n<0?-(n+1):n;r.splice(o,0,t)}function u8(r,t,e){return p8(r,t,e||c8)}function c8(r,t){return r>t?1:r<t?-1:0}function p8(r,t,e){let n=0,o=r.length,s=0,i=!1;for(;n<o;){s=n+(o-n>>>1);let a=e(t,r[s]);a>0?n=s+1:(o=s,i=!a)}return i?n:-n-1}function Cy(r,t,e,n,o){return mN(r,t,e,n,o,0)}function vy(r,t,e,n,o,s){return mN(r,t,e,n,o,0,!1,s,!0)}function Sy(r,t,e,n,o,s){return mN(r,t,e,n,o,s,!0)}function mN(r,t,e,n,o,s,i=!1,a=!1,u=!1){let l=[];for(let g=0;g<t.length;g++)t[g]>o&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(GA);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length<e&&l.length>0;){let g=l.pop(),{score:x,boxIndex:b,suppressBeginIndex:w}=g;if(x<o)break;let I=!1;for(let N=p.length-1;N>=w;--N){let E=m8(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*f8(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,I||(g.score===x?(p.push(b),m.push(g.score)):g.score>o&&VA(l,g,GA))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function m8(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,l),g=Math.max(i,c),x=Math.min(a,p),b=Math.min(u,m),w=Math.max(x-h,0)*Math.max(b-g,0);return w/(f+d-w)}function f8(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function GA(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function d8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,"boxes","nonMaxSuppressionAsync"),i=C(t,"scores","nonMaxSuppressionAsync"),a=_o(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u=await Promise.all([s.data(),i.data()]),l=u[0],c=u[1],{selectedIndices:p}=Cy(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Ke(p,"int32")}var WA=d8;function h8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=_o(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l={boxes:i,scores:a},c={maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=T.runKernel(ol,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var UA=k({nonMaxSuppressionWithScore_:h8});async function g8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=_o(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=Sy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(m,"int32"),selectedScores:Ke(f)}}var HA=g8;function x8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=_o(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(nl,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var qA=k({nonMaxSuppressionPadded_:x8});async function y8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=_o(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=vy(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(d,"int32"),validOutputs:ft(h,"int32")}}var KA=y8;function b8(r,t,e=!1,n=!1){let o=C(r,"images","resizeBilinear");_(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),_(n===!1||e===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(Us,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var Ny=k({resizeBilinear_:b8});function w8(r,t,e=!1,n=!1){let o=C(r,"images","resizeNearestNeighbor");_(o.rank===3||o.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),_(o.dtype==="float32"||o.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),_(n===!1||e===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(Ws,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var ky=k({resizeNearestNeighbor_:w8});function I8(r,t="binary",e=!1,n=.5){let o=C(r,"image","threshold"),s=.2989,i=.587,a=.114,u=o.shape[0]*o.shape[1],l=$(Ke([n]),255),c,p,m,f;if(_(o.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${o.rank}.`),_(o.shape[2]===3||o.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${o.shape[2]}.`),_(o.dtype==="int32"||o.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${o.dtype}.`),_(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),o.shape[2]===3){[c,p,m]=gr(o,[1,1,1],-1);let g=$(c,s),x=$(p,i),b=$(m,a);f=Y(Y(g,x),b)}else f=r;if(t==="otsu"){let g=Tx(Q(bm(f),"int32"),sr([]),256);l=C8(g,u)}let d=e?Un(f,l):Fe(f,l);return Q($(d,255),"int32")}function C8(r,t){let e=Ke([-1]),n=Ke([0]),o=Ke([0]),s,i,a,u,l,c;for(let p=0;p<r.size-1;p++){s=Pt(r,0,p+1),i=Pt(r,p+1),l=ct(pt(s),t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 in cosineDistance: ");let u=ft(1),l=lt(u,pt($(s,i),e,!0));return qr(l,a,o)}var e2=k({cosineDistance_:E8});function A8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;e!=null&&(i=C(e,"weights","hingeLoss")),Re(o.shape,s.shape,"Error in hingeLoss: ");let a=ft(1);o=lt($(ft(2),o),a);let u=Mr(lt(a,$(o,s)));return qr(u,i,n)}var r2=k({hingeLoss_:A8});function D8(r,t,e,n=1,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),a=null;e!=null&&(a=C(e,"weights","huberLoss")),Re(s.shape,i.shape,"Error in huberLoss: ");let u=ft(n),l=Ee(lt(i,s)),c=mo(l,u),p=lt(l,c),m=Y($(ft(.5),Wt(c)),$(u,p));return qr(m,a,o)}var n2=k({huberLoss_:D8});function $8(r,t,e,n=1e-7,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","logLoss"),i=C(t,"predictions","logLoss"),a=null;e!=null&&(a=C(e,"weights","logLoss")),Re(s.shape,i.shape,"Error in logLoss: ");let u=ft(1),l=ft(n),c=Ut($(s,kr(Y(i,l)))),p=$(lt(u,s),kr(Y(lt(u,i),l))),m=lt(c,p);return qr(m,a,o)}var o2=k({logLoss_:$8});function R8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=C(e,"weights","meanSquaredError")),Re(o.shape,s.shape,"Error in meanSquaredError: ");let a=_m(o,s);return qr(a,i,n)}var s2=k({meanSquaredError_:R8});function F8(r,t){let e=C(r,"labels","sigmoidCrossEntropyWithLogits"),n=C(t,"logits","sigmoidCrossEntropyWithLogits");Re(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Mr(n),s=$(n,e),i=Eu(ir(Ut(Ee(n))));return Y(lt(o,s),i)}function O8(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","sigmoidCrossEntropy")),Re(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=ft(n),c=ft(1),p=ft(.5);s=Y($(s,lt(c,l)),$(p,l))}let u=F8(s,i);return qr(u,a,o)}var i2=k({sigmoidCrossEntropy_:O8});function P8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${e}`);return fn((o,s,i)=>{let u=gm(s,[e],!0),l=lt(Q(s,"float32"),u);i([o,l]);let c=Ut($(l,o));return{value:pt(c,[e]),gradFunc:(f,d)=>{let[h,g]=d,x=ko(f.shape,[e]);return[$(R(f,x),lt(Q(h,"float32"),ir(g))),$(R(f,x),lt(ir(g),Q(h,"float32")))]}}})(r,t)}function M8(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"onehotLabels","softmaxCrossEntropy"),i=C(t,"logits","softmaxCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","softmaxCrossEntropy")),Re(s.shape,i.shape,"Error in softmaxCrossEntropy: "),n>0){let l=ft(n),c=ft(1),p=ft(s.shape[1]);s=Y($(s,lt(c,l)),ct(l,p))}let u=P8(s,i);return qr(u,a,o)}var a2=k({softmaxCrossEntropy_:M8});function L8(r,t,e,n){let o=C(r,"indices","sparseFillEmptyRows","int32"),s=C(t,"values","sparseFillEmptyRows"),i=C(e,"denseShape","sparseFillEmptyRows","int32"),a=C(n,"defaultValue","sparseFillEmptyRows",s.dtype);if(o.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
        ${o.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(a.rank!==0)throw new Error(`Default value should be a scalar but received shape ${a.shape}`);let u={indices:o,values:s,denseShape:i,defaultValue:a},l=T.runKernel(cu,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var l2=k({sparseFillEmptyRows_:L8});function z8(r,t,e){let n=C(r,"inputIndices","sparseReshape","int32"),o=C(t,"inputShape","sparseReshape","int32"),s=C(e,"newShape","sparseReshape","int32");if(n.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${n.shape}`);if(o.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${o.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(cl,i);return{outputIndices:a[0],outputShape:a[1]}}var u2=k({sparseReshape_:z8});function B8(r,t,e){let n=C(r,"data","sparseSegmentMean"),o=C(t,"indices","sparseSegmentMean","int32"),s=C(e,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
          ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(pu,i)}var c2=k({sparseSegmentMean_:B8});function V8(r,t,e){let n=C(r,"data","sparseSegmentSum"),o=C(t,"indices","sparseSegmentSum","int32"),s=C(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
         ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
         ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(mu,i)}var p2=k({sparseSegmentSum_:V8});function G8(r,t,e,n,o,s,i,a){let u=C(r,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=C(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:e,nGramWidths:n,leftPad:o,rightPad:s,padWidth:i,preserveShortSequences:a},p={data:u,dataSplits:l},m=T.runKernel(du,p,c);return{nGrams:m[0],nGramsSplits:m[1]}}var m2=k({stringNGrams_:G8});function W8(r,t,e=!0){let n=C(r,"input","stringSplit","string"),o=C(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(o.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${o.shape}`);let s={skipEmpty:e},i={input:n,delimiter:o},a=T.runKernel(hu,i,s);return{indices:a[0],values:a[1],shape:a[2]}}var f2=k({stringSplit_:W8});function U8(r,t){let e=C(r,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let o={input:e};return T.runKernel(gu,o,n)}var d2=k({stringToHashBucketFast_:U8});function H8(r,t,e,n=!0){let o=C(r,"input","staticRegexReplace","string"),s={pattern:t,rewrite:e,replaceGlobal:n};return T.runKernel(cc,{x:o},s)}var h2=k({staticRegexReplace_:H8});var q8={fft:Ou,ifft:vl,rfft:Pu,irfft:Tm},K8={hammingWindow:FA,hannWindow:wy,frame:Iy,stft:OA},hn={flipLeftRight:MA,grayscaleToRGB:LA,resizeNearestNeighbor:ky,resizeBilinear:Ny,rotateWithOffset:zA,cropAndResize:PA,nonMaxSuppression:BA,nonMaxSuppressionAsync:WA,nonMaxSuppressionWithScore:UA,nonMaxSuppressionWithScoreAsync:HA,nonMaxSuppressionPadded:qA,nonMaxSuppressionPaddedAsync:KA,threshold:jA,transform:XA},fN={bandPart:YA,gramSchmidt:ZA,qr:QA},j8={absoluteDifference:t2,computeWeightedLoss:qr,cosineDistance:e2,hingeLoss:r2,huberLoss:n2,logLoss:o2,meanSquaredError:s2,sigmoidCrossEntropy:i2,softmaxCrossEntropy:a2},X8={sparseFillEmptyRows:l2,sparseReshape:u2,sparseSegmentMean:c2,sparseSegmentSum:p2},Y8={stringNGrams:m2,stringSplit:f2,stringToHashBucketFast:d2,staticRegexReplace:h2};var J={};Kt(J,{Serializable:()=>Ch,SerializationMap:()=>ha,registerClass:()=>dN});var Ch=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},ha=class{constructor(){this.classNameMap={}}static getMap(){return ha.instance==null&&(ha.instance=new ha),ha.instance}static register(t){ha.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function dN(r){_(r.className!=null,()=>"Class being registered does not have the static className property defined."),_(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),_(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ha.register(r)}var Kr=class extends Ch{minimize(t,e=!1,n){let{value:o,grads:s}=this.computeGradients(t,n);if(n!=null){let i=n.map(a=>({name:a.name,tensor:s[a.name]}));this.applyGradients(i)}else this.applyGradients(s);return Tt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Kx(t,e)}dispose(){this.iterations_!=null&&Tt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ft(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(Kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var $c=class extends Kr{static get className(){return"Adadelta"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>vt(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=Y($(u,this.rho),$(Wt(a),1-this.rho)),p=$(ct(Ne(Y(l,this.epsilon)),Ne(Y(u,this.epsilon))),a),m=Y($(l,this.rho),$(Wt(p),1-this.rho));u.assign(c),l.assign(m);let f=Y($(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Tt(this.accumulatedGrads.map(t=>t.variable)),Tt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Rc=class extends Kr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>No(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=Y(a,Wt(i));a.assign(u);let l=Y($(ct(i,Ne(Y(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Fc=class extends Kr{static get className(){return"Adam"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=ft(e).variable(),this.accBeta2=ft(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=lt(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=Y($(c,this.beta1),$(l,1-this.beta1)),f=Y($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ct(m,n),h=ct(f,o);c.assign(m),p.assign(f);let g=Y($(ct(d,Y(Ne(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(pn(this.beta1,this.iterations_+1)),this.accBeta2.assign(pn(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var Oc=class extends Kr{static get className(){return"Adamax"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=ft(0).variable(),this.accBeta1=ft(e).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=ct(-this.learningRate,Y($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=Y($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=Ee(l),h=_n(f,d);c.assign(m),p.assign(h);let g=Y($(ct(o,n),ct(m,Y(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(Y(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Sl=class extends Kr{static get className(){return"SGD"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=T.registeredVariables[n];B(()=>{let a=Y($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=$e(ft(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var Pc=class extends Sl{static get className(){return"Momentum"}constructor(t,e,n=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=n,this.accumulations=[],this.m=ft(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulations[o]==null&&(this.accumulations[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(!1))});let i=this.accumulations[o].variable,a=Array.isArray(t)?t[o].tensor:t[n];a!=null&&B(()=>{let u,l=Y($(this.m,i),a);this.useNesterov?u=Y($(this.c,Y(a,$(l,this.m))),s):u=Y($(this.c,l),s),i.assign(l),s.assign(u)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Tt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var Mc=class extends Kr{static get className(){return"RMSProp"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=Y($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=Y($(p,this.decay),$(a,1-this.decay)),f=ct($(a,this.learningRate),Ne(lt(c,Y(Wt(m),this.epsilon)))),d=Y($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=lt(s,d);s.assign(h)}else{let p=Y($(u,this.decay),$(Wt(a),1-this.decay)),m=Y($(l,this.momentum),ct($(a,this.learningRate),Ne(Y(p,this.epsilon))));u.assign(p),l.assign(m);let f=lt(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var Z8=[$c,Rc,Fc,Oc,Pc,Mc,Sl];function g2(){for(let r of Z8)dN(r)}var Lr={};Kt(Lr,{CompositeArrayBuffer:()=>vr,browserFiles:()=>y2,browserHTTPRequest:()=>I2,concatenateArrayBuffers:()=>rE,copyModel:()=>gE,decodeWeights:()=>lx,encodeWeights:()=>Q_,fromMemory:()=>C2,fromMemorySync:()=>wN,getLoadHandlers:()=>iE,getModelArtifactsForJSON:()=>nm,getModelArtifactsForJSONSync:()=>B0,getModelArtifactsInfoForJSON:()=>ea,getSaveHandlers:()=>sE,getWeightSpecs:()=>cx,http:()=>_y,isHTTPScheme:()=>Ty,listModels:()=>dE,loadWeights:()=>b2,moveModel:()=>xE,registerLoadRouter:()=>oE,registerSaveRouter:()=>nE,removeModel:()=>hE,weightsLoaderFactory:()=>yN,withSaveHandler:()=>v2,withSaveHandlerSync:()=>S2});var J8="model",Q8=".json",tY=".weights.bin";function x2(r){return new Promise(t=>setTimeout(t)).then(r)}var Nl=class{constructor(t){if(!L().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");t.startsWith(Nl.URL_SCHEME)&&(t=t.slice(Nl.URL_SCHEME.length)),(t==null||t.length===0)&&(t=J8),this.modelJsonFileName=t+Q8,this.weightDataFileName=t+tY}async save(t){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let e=vr.join(t.weightData),n=window.URL.createObjectURL(new Blob([e],{type:"application/octet-stream"}));if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let o=[{paths:["./"+this.weightDataFileName],weights:t.weightSpecs}],s=ux(t,o),i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=i,await x2(()=>a.dispatchEvent(new MouseEvent("click"))),t.weightData!=null){let u=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;u.download=this.weightDataFileName,u.href=n,await x2(()=>u.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:ea(t)}}}};Nl.URL_SCHEME="downloads://";var hN=class{constructor(t){if(t==null||t.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${t}`);this.jsonFile=t[0],this.weightsFiles=t.slice(1)}async load(){return new Promise((t,e)=>{let n=new FileReader;n.onload=o=>{let s=JSON.parse(o.target.result),i=s.modelTopology;if(i==null){e(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){e(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){t({modelTopology:i});return}let u=nm(s,l=>this.loadWeights(l));t(u)},n.onerror=o=>e(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})}loadWeights(t){let e=[],n=[];for(let i of t)e.push(...i.weights),n.push(...i.paths);let o=this.checkManifestAndWeightFiles(t),s=n.map(i=>this.loadWeightsFile(i,o[i]));return Promise.all(s).then(i=>[e,i])}loadWeightsFile(t,e){return new Promise((n,o)=>{let s=new FileReader;s.onload=i=>{let a=i.target.result;n(a)},s.onerror=i=>o(`Failed to weights data from file of path '${t}'.`),s.readAsArrayBuffer(e)})}checkManifestAndWeightFiles(t){let e=[],n=this.weightsFiles.map(s=>z0(s.name)),o={};for(let s of t)s.paths.forEach(i=>{let a=z0(i);if(e.indexOf(a)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${a}'`);if(e.push(a),n.indexOf(a)===-1)throw new Error(`Weight file with basename '${a}' is not provided.`);o[i]=this.weightsFiles[n.indexOf(a)]});if(e.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${e.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return o}},eY=r=>L().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(Nl.URL_SCHEME)?rY(r.slice(Nl.URL_SCHE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Manifest JSON has weights with names: ${a.join(", ")}.`)}let u=o.reduce((f,d,h)=>(d&&f.push(h),f),[]),l=[];u.forEach(f=>{t[f].paths.forEach(d=>{let h=e+(e.endsWith("/")?"":"/")+d;l.push(h)})});let c=await r(l),p={},m=0;return u.forEach(f=>{let d=t[f].paths.length,h=new vr(c.slice(m,m+d));s[f].forEach(x=>{let b=h.slice(x.groupOffset,x.groupOffset+x.sizeBytes),w=lx(b,[x.manifestEntry]);for(let I in w)p[I]=w[I]}),m+=d}),p}}var nY="application/octet-stream",oY="application/json",vh=class{constructor(t,e){if(this.DEFAULT_METHOD="POST",e==null&&(e={}),this.weightPathPrefix=e.weightPathPrefix,this.onProgress=e.onProgress,this.weightUrlConverter=e.weightUrlConverter,e.fetchFunc!=null?(_(typeof e.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=e.fetchFunc):this.fetch=L().platform.fetch,_(t!=null&&t.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(t)&&_(t.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${t.length}).`),this.path=t,e.requestInit!=null&&e.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=e.requestInit||{}}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let e=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);e.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:t.weightSpecs}],o=ux(t,n);if(e.body.append("model.json",new Blob([JSON.stringify(o)],{type:oY}),"model.json"),t.weightData!=null){let i=vr.join(t.weightData);e.body.append("model.weights.bin",new Blob([i],{type:nY}),"model.weights.bin")}let s=await this.fetch(this.path,e);if(s.ok)return{modelArtifactsInfo:ea(t),responses:[s]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.status}.`)}async load(){let t=await this.fetch(this.path,this.requestInit);if(!t.ok)throw new Error(`Request to ${this.path} failed with status code ${t.status}. Please verify this URL points to the model JSON of the model to load.`);let e;try{e=await t.json()}catch(s){let i=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?i+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":i+=" Please make sure the server is serving valid JSON for this request.",new Error(i)}let n=e.modelTopology,o=e.weightsManifest;if(n==null&&o==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return nm(e,s=>this.loadWeights(s))}async loadWeights(t){let e=Array.isArray(this.path)?this.path[1]:this.path,[n,o]=sY(e),s=this.weightPathPrefix||n,i=cx(t),a=[],u=[];for(let c of t)for(let p of c.paths)this.weightUrlConverter!=null?u.push(this.weightUrlConverter(p)):a.push(s+p+o);this.weightUrlConverter&&a.push(...await Promise.all(u));let l=await xN(a,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[i,l]}};vh.URL_SCHEME_REGEX=/^https?:\/\//;function sY(r){let t=r.lastIndexOf("/"),e=r.lastIndexOf("?"),n=r.substring(0,t),o=e>t?r.substring(e):"";return[n+"/",o]}function Ty(r){return r.match(vh.URL_SCHEME_REGEX)!=null}var w2=(r,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let e=!0;if(Array.isArray(r)?e=r.every(n=>Ty(n)):e=Ty(r),e)return _y(r,t)}return null};ve.registerSaveRouter(w2);ve.registerLoadRouter(w2);function _y(r,t){return new vh(r,t)}function I2(r,t){return _y(r,t)}var Sh=class{constructor(t){this.modelArtifacts=t}load(){return this.modelArtifacts}},Ey=class{constructor(t){this.saveHandler=t}save(t){return this.saveHandler(t)}},bN=class{constructor(t){t.load&&(this.load=()=>Promise.resolve(t.load())),t.save&&(this.save=e=>Promise.resolve(t.save(e)))}};function C2(r,t,e,n){let o=arguments;return new bN(wN(...o))}function wN(r,t,e,n){return arguments.length===1?r.modelTopology!=null||r.weightSpecs!=null?new Sh(r):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new Sh({modelTopology:r})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new Sh({modelTopology:r,weightSpecs:t,weightData:e,trainingConfig:n}))}function v2(r){return new Ey(r)}function S2(r){return new Ey(r)}var k2={};Kt(k2,{confusionMatrix:()=>N2});function iY(r,t,e){let n=C(r,"labels","confusionMatrix"),o=C(t,"predictions","confusionMatrix");_(e==null||e>0&&Number.isInteger(e),()=>`If provided, numClasses must be a positive integer, but got ${e}`),_(n.rank===1,()=>`Expected the rank of labels to be 1, but got ${n.rank}`),_(o.rank===1,()=>`Expected the rank of predictions to be 1, but got ${o.rank}`),_(n.shape[0]===o.shape[0],()=>`Mismatch in the number of examples: ${n.shape[0]} vs. ${o.shape[0]}. Labels and predictions should have the same number of elements.`),_(e>0&&Number.isInteger(e),()=>`numClasses is required to be a positive integer, but got ${e}`);let s=fa(Q(n,"int32"),e),i=fa(Q(o,"int32"),e),a=Vt(s),u=Bt(a,i);return Q(u,"int32")}var N2=k({confusionMatrix_:iY});var Ay={};Kt(Ay,{draw:()=>dY,fromPixels:()=>hY,fromPixelsAsync:()=>pY,toPixels:()=>fY});var Lc,T2=!1;function _2(r,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let e=!1,n=!1,o=!1,s=!1,i=!1,a=!1;if(r.data instanceof Uint8Array)e=!0;else if(typeof ImageData!="undefined"&&r instanceof ImageData)n=!0;else if(typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement)o=!0;else if(typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement)s=!0;else if(r.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap)a=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r.constructor.name}`);if(ih(oh,T.backendName)!=null){let d={pixels:r},h={numChannels:t};return T.runKernel(oh,d,h)}let[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p;if(i)p=r.getContext("2d").getImageData(0,0,l,c).data;else if(n||e)p=r.data;else if(s||o||a){if(Lc==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")Lc=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else Lc=document.createElement("canvas").getContext("2d",{willReadFrequently:!0});Lc.canvas.width=l,Lc.canvas.height=c,Lc.drawImage(r,0,0,l,c),p=Lc.getImageData(0,0,l,c).data}let m;if(t===4)m=new Int32Array(p);else{let d=l*c;m=new Int32Array(d*t);for(let h=0;h<d;h++)for(let g=0;g<t;++g)m[h*t+g]=p[h*4+g]}return my(m,[c,l,t],"int32")}function aY(r){return r!=null&&r.data instanceof Uint8Array}function lY(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function uY(r){return r!=null&&r.width!==0&&r.height!==0}function cY(r){return lY()&&!(r instanceof ImageBitmap)&&uY(r)&&!aY(r)}async function pY(r,t=3){let e=null;if(L().getBool("WRAP_TO_IMAGEBITMAP")&&cY(r)){let n;try{n=await createImageBitmap(r,{premultiplyAlpha:"none"})}catch(o){n=null}n!=null&&n.width===r.width&&n.height===r.height?e=n:e=r}else e=r;return _2(e,t)}function E2(r){if(r.rank!==2&&r.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r.rank}.`);let t=r.rank===2?1:r.shape[2];if(t>4||t===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${t}`);if(r.dtype!=="float32"&&r.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${r.dtype}. Please use float32 or int32 tensors.`)}function mY(r){let t=(r==null?void 0:r.alpha)||1;if(t>1||t<0)throw new Error(`Alpha value ${t} is suppoed to be in range [0 - 1].`)}async function fY(r,t){let e=C(r,"img","toPixels");if(!(r instanceof Ot)){let l=e;e=Q(l,"int32"),l.dispose()}E2(e);let[n,o]=e.shape.slice(0,2),s=e.rank===2?1:e.shape[2],i=await e.data(),a=e.dtype==="float32"?255:1,u=new Uint8ClampedArray(o*n*4);for(let l=0;l<n*o;++l){let c=[0,0,0,255];for(let m=0;m<s;m++){let f=i[l*s+m];if(e.dtype==="float32"){if(f<0||f>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${f}.`)}else if(e.dtype==="int32"&&(f<0||f>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${f}.`);s===1?(c[0]=f*a,c[1]=f*a,c[2]=f*a):c[m]=f*a}let p=l*4;u[p+0]=Math.round(c[0]),u[p+1]=Math.round(c[1]),u[p+2]=Math.round(c[2]),u[p+3]=Math.round(c[3])}if(t!=null){T2||(console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."),T2=!0),t.width=o,t.height=n;let l=t.getContext("2d"),c=new ImageData(u,o,n);l.putImageData(c,0,0)}return e!==r&&e.dispose(),u}function dY(r,t,e){let n=C(r,"img","draw");if(!(r instanceof Ot)){let i=n;n=Q(i,"int32"),i.dispose()}E2(n),mY(e==null?void 0:e.imageOptions);let o={image:n},s={canvas:t,options:e};T.runKernel(Zg,o,s)}var hY=k({fromPixels_:_2});var Dy={};Kt(Dy,{prepareAndValidate:()=>A2});function A2(r,t){let e=r.shape.length,n=t.shape.length;if(e<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e}.`);if(n<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${n}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[n-1]>e)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[n-1]} vs. ${e}`);if(te(r.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`);let o=t.shape,s=o[o.length-1],i=1;for(let p=0;p<o.length-1;++p)i*=o[p];let a=r.shape,u=o.slice();u.pop();let l=1;for(let p=s;p<e;++p)l*=a[p],u.push(a[p]);let c=[...Di(r.shape).map(p=>p/l),1].slice(0,s);return[u,i,l,c]}var ze={};Kt(ze,{assertParamsValid:()=>xY,computeFlatOffset:()=>CY,computeOutShape:()=>bY,getNormalizedAxes:()=>wY,isSliceContinous:()=>IY,maskToAxes:()=>yY,parseSliceParams:()=>CN,sliceInfo:()=>vY,startForAxis:()=>L2,startIndicesWithElidedDims:()=>O2,stopForAxis:()=>z2,stopIndicesWithElidedDims:()=>P2,stridesForAxis:()=>M2,stridesWithElidedDims:()=>$2});var IN=-2,gY=-1;function xY(r,t,e){let n=r.shape.length;_(n===t.length,()=>`Error in slice${n}D: Length of begin ${t} must match the rank of the array (${n}).`),_(n===e.length,()=>`Error in slice${n}D: Length of size ${e} must match the rank of the array (${n}).`);for(let o=0;o<n;++o)_(t[o]+e[o]<=r.shape[o],()=>`Error in slice${n}D: begin[${o}] + size[${o}] (${t[o]+e[o]}) would overflow input.shape[${o}] (${r.shape[o]})`)}function yY(r){let t=[],e=0;for(;r>0;)r&1&&t.push(e),r/=2,e++;return t}function bY(r,t,e){let n=[];for(let o=0;o<r.length;o++)n[o]=Math.ceil((t[o]-r[o])/e[o]);return n}function $2(r,t,e,n){let o=[...r];for(let s=o.length;s<n.length;s++)o.push(1);for(let s=0;s<e;s++)s===0?o[t]=1:(o.splice(t,0,1),o.pop());return o}function R2(r,t,e){return e<=r?e:e-(t-1)}function F2(r,t){let e=[];for(let n=0;n<r;n++)e.push(t+n);return e}function wY(r,t,e,n,o,s,i,a,u){let l=r.length,c=new Array(l),p=new Array(l),m=new Array(l);if(t.length&&e>0){let f=t[0],d=e+1;c=O2(i,f,d,n,r),p=P2(a,f,d,o,r),m=$2(s,f,d,r)}else for(let f=0;f<l;f++)c[f]=L2(i,n,s,r,f,u),p[f]=z2(a,o,s,r,f,u),m[f]=M2(s,f,u);return{begin:c,end:p,strides:m}}function O2(r,t,e,n,o){let s=[...o],i=F2(e,t);for(let a=0;a<s.length;a++)if(i.indexOf(a)>-1)s[a]=0;else{let u=R2(t,e,a),l=n[u];r&1<<u&&(l=0),s[a]=l}return s}function P2(r,t,e,n,o){let s=[...o],i=F2(e,t);for(let a=0;a<s.length;a++)if(i.indexOf(a)>-1)s[a]=Number.MAX_SAFE_INTEGER;else{let u=R2(t,e,a),l=n[u];r&1<<u&&(l=Number.MAX_SAFE_INTEGER),s[a]=l}for(let a=0;a<s.length;a++){let u=o[a];s[a]<0&&(s[a]+=u),s[a]=Op(0,s[a],o[a])}return s}function M2(r,t,e){let n=r[t];return(e&1<<t||n==null)&&(n=1),n}function L2(r,t,e,n,o,s){let i=t[o],a=e[o]||1;(r&1<<o||s&1<<o||i==null)&&(a>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let u=n[o];return i<0&&(i+=u),i=Op(0,i,u-1),i}function z2(r,t,e,n,o,s){let i=t[o],a=e[o]||1;(r&1<<o||s&1<<o||i==null)&&(a>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let u=n[o];return i<0&&(i+=u),a>0?i=Op(0,i,u):i=Op(-1,i,u-1),i}function IY(r,t,e){let n=e.length;for(let o=0;o<e.length;o++)if(e[o]>1){n=o;break}for(let o=n+1;o<e.length;o++)if(t[o]>0||e[o]!==r[o])return!1;return!0}function CY(r,t){let e=r.length>0?r[r.length-1]:1;for(let n=0;n<r.length-1;n++)e+=r[n]*t[n];return e}function CN(r,t,e){let n,o=r.shape.length;typeof t=="number"?n=[t,...new Array(o-1).fill(0)]:t.length<o?n=t.concat(new Array(o-t.length).fill(0)):n=t.slice(),n.forEach(i=>{_(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return e==null?s=new Array(o).fill(-1):typeof e=="number"?s=[e,...new Array(o-1).fill(-1)]:e.length<o?s=e.concat(new Array(o-e.length).fill(-1)):s=e,s=s.map((i,a)=>i>=0?i:(_(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${a}.`),r.shape[a]-n[a])),[n,s]}function vY(r,t,e,n,o,s,i,a,u){let l;if(n==null?(l=new Array(t.length),l.fill(1)):l=n,i!=null&&i&i-1)throw new Error("Multiple ellipses in slice is not allowed.");let c=!1,p={dims:l.length,numAddAxisAfterEllipsis:0,begin:t.slice(),end:e.slice(),strides:l.slice(),beginMask:o,endMask:s,ellipsisMask:i,newAxisMask:a,shrinkAxisMask:u};for(let w=0;w<p.dims;w++)c&&1<<w&a&&p.numAddAxisAfterEllipsis++,1<<w&i&&(c=!0);c||(p.ellipsisMask|=1<<p.dims,p.dims++);let m={dims:r.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};SY(p,m);let f=!0,d=!0,h=!0,g=[],x=[];for(let w=0;w<r.length;++w){if(m.strides[w]===0)throw Error(`strides[${w}] must be non-zero`);let I=!!(m.shrinkAxisMask&1<<w),N=r[w];if(N===-1){g.push(I?1:-1);continue}let E=[m.beginMask&1<<w,m.endMask&1<<w],A=[m.strides[w]>0?0:-1,m.strides[w]>0?N:N-1];if(I&&m.strides[w]<=0)throw Error("only stride 1 allowed on non-range indexing.");h=h&&m.strides[w]===1;let D=!!(m.beginMask&1<<w&&m.endMask&1<<w);if(m.beginValid&&m.endValid){if(I){let G=m.begin[w]<0?N+m.begin[w]:m.begin[w];if(m.begin[w]=G,m.end[w]=m.begin[w]+1,G<0||G>=N)throw Error(`slice index ${m.begin[w]} of dimension ${w} out of bounds.`)}else m.begin[w]=D2(m.begin[w],0,m.strides[w],N,E,A),m.end[w]=D2(m.end[w],1,m.strides[w],N,E,A);let V=m.strides[w]===1&&m.begin[w]===0&&m.end[w]===N;f=f&&V,d=d&&(w===0&&m.strides[w]===1||V)}else f=f&&m.strides[w]===1&&D,d=d&&(w===0&&m.strides[w]===1||D);let F,P=!1;if(m.beginValid&&m.endValid?(F=m.end[w]-m.begin[w],P=!0):I?(F=1,P=!0):D&&N>=0&&(m.strides[w]<0?F=-N:F=N,P=!0),P){let V;F===0||F<0!=m.strides[w]<0?V=0:V=Math.trunc(F/m.strides[w])+(F%m.strides[w]!==0?1:0),g.push(V)}else g.push(-1)}for(let w=0;w<m.finalShapeGatherIndices.length;++w){let I=m.finalShapeGatherIndices[w];I>=0?x.push(g[I]):I===IN&&x.push(1)}return{finalShapeSparse:x.filter((w,I)=>m.finalShapeGatherIndices[I]!==IN),finalShape:x,isIdentity:f,sliceDim0:d,isSimpleSlice:h,begin:m.begin,end:m.end,strides:m.strides}}function SY(r,t){t.beginMask=0,t.endMask=0,t.shrinkAxisMask=0;let e=0;t.beginValid=r.begin!=null,t.endValid=r.end!=null,t.begin=new Array(t.dims),t.end=new Array(t.dims),t.strides=new Array(t.dims),t.finalShapeGatherIndices=[],t.finalShapeGatherIndicesSparse=[],t.inputShapeGatherIndicesSparse=new Array(t.dims);for(let n=0;n<r.dims;n++)if(1<<n&r.ellipsisMask){let o=Math.min(t.dims-(r.dims-n)+1+r.numAddAxisAfterEllipsis,t.dims);for(;e<o;e++)t.begin[e]=0,t.end[e]=0,t.strides[e]=1,t.beginMask|=1<<e,t.endMask|=1<<e,t.finalShapeGatherIndices.push(e),t.finalShapeGatherIndicesSparse.push(-1),t.inputShapeGatherIndicesSparse[e]=n}else if(1<<n&r.newAxisMask)t.finalShapeGatherIndices.push(IN),t.finalShapeGatherIndicesSparse.push(-1);else{if(e===t.begin.length)throw Error(`Index out of range using input dim ${e}; input has only ${t.dims} dims, ${t.begin.length}.`);r.begin!=null&&(t.begin[e]=r.begin[n]),r.end!=null&&(t.end[e]=r.end[n]),t.strides[e]=r.strides[n],r.beginMask&1<<n&&(t.beginMask|=1<<e),r.endMask&1<<n&&(t.endMask|=1<<e),r.shrinkAxisMask&1<<n?(t.finalShapeGatherIndices.push(gY),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<e):(t.finalShapeGatherIndices.push(e),t.finalShapeGatherIndicesSparse.push(n)),t.inputShapeGatherIndicesSparse[e]=n,e++}}function D2(r,t,e,n,o,s){if(o[t])return e>0?s[t]:s[t+1&1];{let i=r<0?n+r:r;return i<s[0]?s[0]:i>s[1]?s[1]:i}}var B2="4.7.0";var Nh=class{static sgd(t){return new Sl(t)}static momentum(t,e,n=!1){return new Pc(t,e,n)}static rmsprop(t,e=.9,n=0,o=null,s=!1){return new Mc(t,e,n,o,s)}static adam(t=.001,e=.9,n=.999,o=null){return new Fc(t,e,n,o)}static adadelta(t=.001,e=.95,n=null){return new $c(t,e,n)}static adamax(t=.002,e=.9,n=.999,o=null,s=0){return new Oc(t,e,n,o,s)}static adagrad(t,e=.1){return new Rc(t,e)}};var zc=Nh;var NY=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function kh(){return new Promise(r=>NY(()=>r()))}var S={};Kt(S,{ERF_A1:()=>BY,ERF_A2:()=>VY,ERF_A3:()=>GY,ERF_A4:()=>WY,ERF_A5:()=>UY,ERF_P:()=>zY,PARALLELIZE_THRESHOLD:()=>$y,RowPartitionType:()=>ga,SELU_SCALE:()=>SN,SELU_SCALEALPHA:()=>vN,applyActivation:()=>Ac,assertAndGetBroadcastShape:()=>Mt,assertAxesAreInnerMostDims:()=>l6,assertParamsConsistent:()=>kY,assignToTypedArray:()=>YY,axesAreInnerMostDims:()=>Z0,calculateShapes:()=>kA,checkEinsumDimSizes:()=>r7,checkPadOnDimRoundingMode:()=>Se,combineLocations:()=>$E,combineRaggedTensorToTensorShapes:()=>_Y,complexWithEvenIndex:()=>KY,complexWithOddIndex:()=>jY,computeConv2DInfo:()=>wc,computeConv3DInfo:()=>CE,computeDefaultPad:()=>Y0,computeDilation2DInfo:()=>aj,computeOptimalWindowSize:()=>$Y,computeOutAndReduceShapes:()=>J0,computeOutShape:()=>TY,computePool2DInfo:()=>X0,computePool3DInfo:()=>lj,convertConv2DDataFormat:()=>vE,decodeEinsumEquation:()=>t7,eitherStridesOrDilationsAreOne:()=>Rr,expandShapeToKeepDim:()=>ko,exponent:()=>JY,exponents:()=>ZY,fromStringArrayToUint8:()=>v7,fromUint8ToStringArray:()=>C7,getAxesPermutation:()=>Q0,getBroadcastDims:()=>EE,getComplexWithIndex:()=>XY,getEinsumComputePath:()=>n7,getEinsumPermutation:()=>e7,getFusedBiasGradient:()=>Ec,getFusedDyActivation:()=>_c,getImageCenter:()=>RY,getInnerMostAxes:()=>u6,getPermuted:()=>OY,getRaggedRank:()=>AY,getReductionAxes:()=>ye,getReshaped:()=>FY,getReshapedPermuted:()=>PY,getRowPartitionTypesHelper:()=>EY,getSliceBeginCoords:()=>MY,getSliceSize:()=>LY,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>a7,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>l7,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>u7,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>m7,getSparseReshapeInputOutputMismatchErrorMessage:()=>d7,getSparseReshapeInputOutputMultipleErrorMessage:()=>f7,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>c7,getSparseReshapeNegativeOutputDimErrorMessage:()=>p7,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>y7,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>h7,getSparseSegmentRed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real:o,imag:s}}var NN="->",QY=/->/g,V2=",",G2="...";function t7(r,t){r=r.replace(/\s/g,"");let e=(r.length-r.replace(QY,"").length)/NN.length;if(e<1)throw new Error("Equations without an arrow are not supported.");if(e>1)throw new Error(`Equation must contain exactly one arrow ("${NN}").`);let[n,o]=r.split(NN);_(n.indexOf(G2)===-1,()=>`The ellipsis notation ("${G2}") is not supported yet.`);let s=n.split(V2),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let a=[];for(let m=0;m<o.length;++m){let f=o[m];if(!s.some(d=>d.indexOf(f)!==-1))throw new Error(`Output subscripts contain the label ${f} not present in the input subscripts.`);a.indexOf(f)===-1&&a.push(f)}for(let m=0;m<n.length;++m){let f=n[m];a.indexOf(f)===-1&&f!==V2&&a.push(f)}let u=new Array(s.length);for(let m=0;m<i;++m){if(new Set(s[m].split("")).size!==s[m].length)throw new Error(`Found duplicate axes in input component ${s[m]}. Support for duplicate axes in input is not implemented yet.`);u[m]=[];for(let f=0;f<s[m].length;++f)u[m].push(a.indexOf(s[m][f]))}let l=a.length,c=o.length,p=[];for(let m=c;m<l;++m)p.push(m);return{allDims:a,summedDims:p,idDims:u}}function e7(r,t){let e=new Array(r);e.fill(-1);for(let o=0;o<t.length;++o)e[t[o]]=o;let n=[];for(let o=0;o<r;++o)e[o]===-1&&n.push(o);return e=e.filter(o=>o!==-1),{permutationIndices:e,expandDims:n}}function r7(r,t,e){let n=new Array(r);for(let o=0;o<e.length;++o){let s=e[o].shape;for(let i=0;i<t[o].length;++i)n[t[o][i]]===void 0?n[t[o][i]]=s[i]:_(n[t[o][i]]===s[i],()=>`Expected dimension ${n[t[o][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function n7(r,t){let e=r,n=[],o=0;r.length===0&&e.push(-1),o=r.length+1;for(let i=0;i<o;++i)n.push([]);let s=[];for(let i=0;i<e.length;++i){let a=e[i],u=s7(t,a);for(let l of u)s.indexOf(l)===-1&&(n[i].push(l),s.push(l))}return{path:e,steps:n}}function o7(r){return r.every((t,e)=>t===e)}function s7(r,t){let e=[];for(let n=0;n<r.length;++n)(r[n].length===0||r[n].indexOf(t)!==-1||t===-1)&&e.push(n);return e}function i7(r,t,e=0){let n=[];if(typeof t=="number")_(r.shape[e]%t===0,()=>"Number of splits must evenly divide the axis."),n=new Array(t).fill(r.shape[e]/t);else{let o=t.reduce((i,a)=>(a===-1&&(i+=1),i),0);_(o<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((a,u)=>u>0?a+u:a);t[s]=r.shape[e]-i}_(r.shape[e]===t.reduce((i,a)=>i+a),()=>"The sum of sizes must match the size of the axis dimension."),n=t}return n}function a7(r){return`Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${r}`}function l7(r,t){return`indices(${r}, 0) is invalid: ${t} < 0`}function u7(r,t,e){return`indices(${r}, 0) is invalid: ${t} >= ${e}`}function c7(r,t){return`only one output dimension may be -1, not both ${r} and ${t}`}function p7(r,t){return`size ${r} must be non-negative, not ${t}`}function m7(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function f7(r,t){let e=te(r),n=te(t);return`Input to reshape is a SparseTensor with ${e}
  dense values, but the requested shape requires a multiple of ${n}. inputShape=${r} outputShape= ${t}`}function d7(r,t){let e=te(r),n=te(t);return`Input to reshape is a tensor with ${e} dense values, but the requested shape has ${n}. inputShape=${r} outputShape=${t}`}function h7(){return"segment ids must be >= 0"}function g7(){return"segment ids are not increasing"}function x7(r,t){return`Segment id ${r} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function y7(r,t,e){return`Bad: indices[${r}] == ${t} out of range [0, ${e})`}var kN={};Kt(kN,{collectGatherOpShapeInfo:()=>I7,computeOutShape:()=>w7,segOpComputeOptimalWindowSize:()=>b7});function b7(r,t){let e=!1,n;for(r<=$y?(n=r,e=!0):n=Mp(r,Math.floor(Math.sqrt(r)));!e;)n>t||n===r?e=!0:n=Mp(r,n+1);return n}function w7(r,t,e){let n=[],o=r.length;for(let s=0;s<o;s++)s!==t?n.push(r[s]):n.push(e);return n}function I7(r,t,e,n){let o=t.shape.length,s=r.shape.length;if(n!==0&&(n<-o||n>o))throw new Error(`Expect batchDims in the range of [-${o}, ${o}], but got ${n}`);if(n<0&&(n+=o),n>s)throw new Error(`batchDims (${n}) must be less than rank(x) (
    ${s}).`);if(e<n)throw new Error(`batchDims (${n}) must be less than or equal to axis (${e}).`);for(let p=0;p<n;++p)if(r.shape[p]!==t.shape[p])throw new Error(`x.shape[${p}]: ${r.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);let i=r.shape[e],a=[],u=1,l=1,c=1;for(let p=0;p<n;++p)a.push(r.shape[p]),u*=r.shape[p];for(let p=n;p<e;p++)a.push(r.shape[p]),l*=r.shape[p];for(let p=n;p<o;p++)a.push(t.shape[p]);for(let p=e+1;p<s;p++)a.push(r.shape[p]),c*=r.shape[p];return{batchSize:u,sliceSize:c,outerSize:l,dimSize:i,outputShape:a}}function C7(r){try{return r.map(t=>em(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function v7(r){return r.map(t=>wu(t))}var jr={};Kt(jr,{nonMaxSuppressionV3Impl:()=>Cy,nonMaxSuppressionV4Impl:()=>vy,nonMaxSuppressionV5Impl:()=>Sy,whereImpl:()=>gy});g2();var Ry={kernelName:$i,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,To(Q(e,"float32"),-1))}}};var W2={kernelName:qo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=Wt(Q(e,"float32")),o=Ne(lt(ft(1),n));return Ut(ct(r,o))}}}};var U2={kernelName:Ko,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=Ne(lt(Wt(Q(e,"float32")),1));return ct(r,n)}}}};var H2={kernelName:ao,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=r,u=ye(e.shape,o);return u.length>0&&(a=pt(a,u)),R(a,e.shape)},b:()=>{let a=r,u=ye(n.shape,o);return u.length>0&&(a=pt(a,u)),R(a,n.shape)}}}};var q2={kernelName:jo,saveAllInputs:!0,gradFunc:(r,t)=>{let e={};return t.forEach((n,o)=>{e[o]=()=>r.clone()}),e}};var K2={kernelName:Ri,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>vt(e)}}};var j2={kernelName:Fi,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>vt(e)}}};var X2={kernelName:Xo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ct(r,Ne(lt(ft(1),Wt(Q(e,"float32")))))}}};var Y2={kernelName:Yo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=Ne(Y(ft(1),Wt(Q(e,"float32"))));return ct(r,n)}}}};var Z2={kernelName:Qo,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=Y(Wt(e),Wt(n)),u=$(r,ct(n,a)),l=ye(e.shape,o);return l.length>0&&(u=pt(u,l)),R(u,e.shape)},b:()=>{let a=Y(Wt(e),Wt(n)),u=Ut($(r,ct(e,a))),l=ye(n.shape,o);return l.length>0&&(u=pt(u,l)),R(u,n.shape)}}}};var J2={kernelName:Zo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ct(r,Y(Wt(Q(e,"float32")),1))}}};var Q2={kernelName:Jo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ct(r,lt(ft(1),Wt(Q(e,"float32"))))}}};function S7(r,t,e,n,o,s){let i=C(r,"dy","avgPool3dGrad"),a=C(t,"input","avgPool3dGrad"),u=i,l=a,c=!1;a.rank===4&&(c=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),l=R(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),_(u.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),_(l.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`),Se("avgPool3dGrad",o,s);let p={dy:u,input:l},m={filterSize:e,strides:n,pad:o,dimRoundingMode:s},f=T.runKernel(Jl,p,m);return c?R(f,[f.shape[1],f.shape[2],f.shape[3],f.shape[4]]):f}var tD=k({avgPool3dGrad_:S7});var eD={kernelName:Oi,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{filterSize:o,strides:s,pad:i,dimRoundingMode:a}=e;return{x:()=>tD(r,n,o,s,i,a)}}};function N7(r,t,e,n,o){let s=C(r,"dy","avgPoolGrad"),i=C(t,"input","avgPoolGrad");_(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let a=i,u=s,l=!1;i.rank===3&&(l=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),_(u.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`),_(a.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${a.rank}.`);let c={dy:u,input:a},p={filterSize:e,strides:n,pad:o},m=T.runKernel(Zl,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var rD=k({avgPoolGrad_:N7});var nD={kernelName:ts,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{filterSize:o,strides:s,pad:i}=e;return{x:()=>rD(r,n,o,s,i)}}};var oD={kernelName:es,inputsToSave:["a","b"],gradFunc:(r,t,e)=>{let[n,o]=t,{transposeA:s,transposeB:i}=e;return!s&&!i?{a:()=>Bt(r,o,!1,!0),b:()=>Bt(n,r,!0,!1)}:!s&&i?{a:()=>Bt(r,o,!1,!1),b:()=>Bt(r,n,!0,!1)}:s&&!i?{a:()=>Bt(o,r,!1,!0),b:()=>Bt(n,r,!1,!1)}:{a:()=>Bt(o,r,!0,!0),b:()=>Bt(r,n,!0,!0)}}};var sD={kernelName:Pi,gradFunc:(r,t,e)=>{let{blockShape:n,crops:o}=e;return{x:()=>$u(r,n,o)}}};var iD={kernelName:C_,gradFunc:(r,t,e)=>{let n=e,o=n.inputShape,s=n.shape,i=Array.from(s);for(let u=o.length-1;u>=0;u--)if(o[u]===s[u])i[u]=1;else if(o[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${s}].`);let a=[];for(let u=0;u<i.length;u++)i[u]>1&&a.push(u);return{x:()=>pt(r,a,!0)}}};var aD={kernelName:xo,gradFunc:r=>({x:()=>r.clone()})};var lD={kernelName:rs,gradFunc:r=>({x:()=>vt(r)})};var uD={kernelName:yo,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{clipValueMin:o,clipValueMax:s}=e;return{x:()=>be(Pr(mn(n,o),Un(n,s)),r,vt(r))}}};var cD={kernelName:tu,inputsToSave:["x"],gradFunc:Ry.gradFunc};var pD={kernelName:Mi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let n=t.map(u=>u.shape),{axis:o}=e,s=fr(o,t[0].shape)[0],i=n.map(u=>u[s]);return gr(r,i,s).map(u=>()=>u)}};var mD={kernelName:ns,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,{dilations:s,strides:i,pad:a,dataFormat:u}=e;return _(co(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>pm(n.shape,r,o,i,a,u),filter:()=>$m(n,r,o.shape,i,a,u)}}};var fD={kernelName:os,inputsToSave:["dy","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,{strides:s,pad:i,dataFormat:a,dimRoundingMode:u}=e;return{dy:()=>Tn(r,o,s,i,a,1,u),filter:()=>$m(r,n,o.shape,s,i,a,u)}}};function k7(r,t,e,n,o){let s=r;r.rank===4&&(s=R(r,[1,r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));let i=t;i.rank===4&&(i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),_(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),_(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),_(e.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${e}.`),_(s.shape[4]===e[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${e[3]}.`),_(i.shape[4]===e[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${e[4]}).`);let a={x:s,dy:i},u={strides:n,pad:o,filterShape:e};return T.runKernel(Ma,a,u)}var dD=k({conv3DBackpropFilter_:k7});var hD={kernelName:ss,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let{dilations:n,strides:o,pad:s}=e;_(co(n),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${n}'`);let[i,a]=t;return{x:()=>Fx(i.shape,r,a,o,s),filter:()=>dD(i,r,a.shape,o,s)}}};var gD={kernelName:is,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(Ut(vm(Q(e,"float32"))),r)}}};var xD={kernelName:as,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(Sm(Q(e,"float32")),r)}}};var yD={kernelName:ls,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{axis:o,exclusive:s,reverse:i}=e;return{x:()=>{let a=Q0([o],n.rank),u=dm(r,o,s,!i);return a!=null&&(u=Vt(u,a)),u}}}};var bD={kernelName:us,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let{dilations:n,strides:o,pad:s,dimRoundingMode:i}=e,a=n==null?[1,1]:n;_(co(a),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[u,l]=t;return _(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`),_(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`),_(u.shape[3]===l.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),_(Rr(o,a),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${o} and dilations '${a}'.`),Se("depthwiseConv2d",s,i),{x:()=>by(u.shape,r,l,o,s,a,i),filter:()=>yy(u,r,l.shape,o,s,a,i)}}};var wD={kernelName:cs,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,s={x:n,filter:o,dy:r},i={x:n,filter:o,dy:r};return{x:()=>T.runKernel(nu,s,e),filter:()=>T.runKernel(ou,i,e)}}};var ID={kernelName:ms,outputsToSave:[!0],gradFunc:(r,t)=>{let[e]=t,n={dy:r,y:e};return{x:()=>T.runKernel(Ga,n)}}};var CD={kernelName:fs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t,n=$(ir(Ut(Wt(e))),2/Math.sqrt(Math.PI));return{x:()=>$(r,n)}}};var vD={kernelName:ds,outputsToSave:[!0],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,e)}}};var SD={kernelName:Li,inputsToSave:["input"],gradFunc:(r,t)=>{let[e]=t;return{input:()=>R(r,e.shape)}}};var ND={kernelName:hs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,ir(e))}}};var kD={kernelName:gs,gradFunc:r=>({x:()=>vt(r)})};var TD={kernelName:xs,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=ct(r,Q(n,"float32")),u=ye(e.shape,o);return u.length>0?R(pt(a,u),e.shape):a},b:()=>{let a=$(r,Q(e,"float32")),u=ye(n.shape,o);u.length>0&&(a=R(pt(a,u),n.shape));let l=Wt(n);return Ut(ct(a,Q(l,"float32")))}}}};var _D={kernelName:ys,inputsToSave:["x","mean","variance","scale"],gradFunc:(r,t,e)=>{let{varianceEpsilon:n}=e,[o,s,i,a]=t,u=a==null?ft(1):a,l=ye(s.shape,o.shape),c=[];if(s.rank===1){for(let I=0;I<o.shape.length-1;++I)c.push(o.shape[I]);c.push(1)}let p=lt(o,s),m=$(r,u),f=wm(Y(i,ft(n))),d=$($($(f,f),f),ft(-.5));return{x:()=>s.rank===1?R($($(r,Or(R(f,[1,1,1,s.shape[0]]),c)),u),o.shape):R($($(r,f),u),o.shape),mean:()=>{let I=$($(f,ft(-1)),m);return s.rank===1&&(I=pt(I,l)),R(I,s.shape)},variance:()=>{let I=$($(d,p),m);return s.rank===1&&(I=pt(I,l)),R(I,s.shape)},scale:()=>{let I=$(p,f),N=$(r,I);ret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 Am(t,this.mean,this.stddev,e,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Gm.className="TruncatedNormal";J.registerClass(Gm);var Wm=class extends wn{constructor(t){super(),this.gain=t.gain!=null?t.gain:1}apply(t,e){return B(()=>{if(t.length!==2||t[0]!==t[1])throw new z("Identity matrix initializer can only be used for 2D square matrices.");return $(this.gain,Cc(t[0]))})}getConfig(){return{gain:this.gain}}};Wm.className="Identity";J.registerClass(Wm);function B7(r,t="channelsLast"){let e,n;if(Oe(t),r.length===2)e=r[0],n=r[1];else if([3,4,5].indexOf(r.length)!==-1){if(t==="channelsFirst"){let o=Ro(r,2);e=r[1]*o,n=r[0]*o}else if(t==="channelsLast"){let o=Ro(r,0,r.length-2);e=r[r.length-2]*o,n=r[r.length-1]*o}}else{let o=Ro(r);e=Math.sqrt(o),n=Math.sqrt(o)}return[e,n]}var Yr=class extends wn{constructor(t){if(super(),t.scale<0)throw new z(`scale must be a positive float. Got: ${t.scale}`);this.scale=t.scale==null?1:t.scale,this.mode=t.mode==null?"fanIn":t.mode,L7(this.mode),this.distribution=t.distribution==null?"normal":t.distribution,z7(this.distribution),this.seed=t.seed}apply(t,e){let n=B7(t),o=n[0],s=n[1],i=this.scale;if(this.mode==="fanIn"?i/=Math.max(1,o):this.mode==="fanOut"?i/=Math.max(1,s):i/=Math.max(1,(o+s)/2),this.distribution==="normal"){let a=Math.sqrt(i);if(e=e||"float32",e!=="float32"&&e!=="int32")throw new kt(`${this.getClassName()} does not support dType ${e}.`);return Am(t,0,a,e,this.seed)}else{let a=Math.sqrt(3*i);return Hn(t,-a,a,e,this.seed)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Yr.className="VarianceScaling";J.registerClass(Yr);var Gc=class extends Yr{constructor(t){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Gc.className="GlorotUniform";J.registerClass(Gc);var Wc=class extends Yr{constructor(t){super({scale:1,mode:"fanAvg",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Wc.className="GlorotNormal";J.registerClass(Wc);var Uc=class extends Yr{constructor(t){super({scale:2,mode:"fanIn",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Uc.className="HeNormal";J.registerClass(Uc);var Hc=class extends Yr{constructor(t){super({scale:2,mode:"fanIn",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Hc.className="HeUniform";J.registerClass(Hc);var qc=class extends Yr{constructor(t){super({scale:1,mode:"fanIn",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};qc.className="LeCunNormal";J.registerClass(qc);var Kc=class extends Yr{constructor(t){super({scale:1,mode:"fanIn",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Kc.className="LeCunUniform";J.registerClass(Kc);var Um=class extends wn{constructor(t){super(),this.DEFAULT_GAIN=1,this.ELEMENTS_WARN_SLOW=2e3,this.gain=t.gain==null?this.DEFAULT_GAIN:t.gain,this.seed=t.seed}apply(t,e){return B(()=>{if(t.length<2)throw new kt("Shape must be at least 2D.");if(e!=="int32"&&e!=="float32"&&e!==void 0)throw new TypeError(`Unsupported data type ${e}.`);e=e;let n=y.sizeFromShape(t.slice(0,-1)),o=t[t.length-1],s=n*o;s>this.ELEMENTS_WARN_SLOW&&console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${s}) elements: Slowness may result.`);let i=[Math.max(o,n),Math.min(o,n)],a=Mm(i,0,1,e,this.seed),u=fN.qr(a,!1),l=u[0],p=u[1].flatten().stridedSlice([0],[Math.min(o,n)*Math.min(o,n)],[Math.min(o,n)+1]);return l=$(l,p.sign()),n<o&&(l=l.transpose()),$(ft(this.gain),l.reshape(t))})}getConfig(){return{gain:this.gain,seed:this.seed}}};Um.className="Orthogonal";J.registerClass(Um);var sR={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function iR(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,"initializer")}function _e(r){return Fm(r)}function he(r){if(typeof r=="string"){let t=r in sR?sR[r]:r;if(t==="GlorotNormal")return new Wc;if(t==="GlorotUniform")return new Gc;if(t==="HeNormal")return new Uc;if(t==="HeUniform")return new Hc;if(t==="LeCunNormal")return new qc;if(t==="LeCunUniform")return new Kc;{let e={};return e.className=t,e.config={},iR(e)}}else return r instanceof wn?r:iR(r)}function Hy(r){return Array.isArray(r)&&Array.isArray(r[0])}function Hm(r){return r.length===0?[]:Array.isArray(r[0])?r:[r]}function St(r){let t;if(Array.isArray(r)){if(r.length!==1)throw new z(`Expected Tensor length to be 1; got ${r.length}`);t=r[0]}else t=r;return t}function Gt(r){if(Array.isArray(r)&&Array.isArray(r[0])){if(r.length===1)return r=r,r[0];throw new z(`Expected exactly 1 Shape; got ${r.length}`)}else return r}function qm(r){let t=0;for(let e of r)e.shape.length===0?t+=1:t+=e.shape.reduce((n,o)=>n*o);return t}var lR="Variable",Dh=class{constructor(t,e="float32",n=lR,o=!0,s=null){this.dtype=e==null?"float32":e,this.shape=t.shape,this.id=Ly(),n=n==null?lR:n,this.originalName=zy(n),this.name=By(this.originalName),this.trainable_=o,this.constraint=s,this.val=hy(t,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(t){return this.assertNotDisposed(),G7(this.val,t),this.val.id!==t.id&&(this.val.assign(t),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(t){this.trainable_=t,this.val.trainable=t}};function G7(r,t){if(r.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(r.shape)+" vs. "+JSON.stringify(t.shape))}function $h(r){return r.map(t=>t.read())}function Km(r){r.forEach(t=>{t[0].write(t[1])})}var Ie=class{constructor(t){this.dtype=t.dtype,this.shape=t.shape,t.shape!=null?this.ndim=t.shape.length:this.ndim=t.ndim,this.maxNDim=t.maxNDim,this.minNDim=t.minNDim,this.axes=t.axes||{}}},nn=class{constructor(t,e,n,o,s,i,a){this.dtype=t,this.shape=e,this.sourceLayer=n,this.inputs=o,this.callArgs=s,this.outputTensorIndex=a,this.id=Ly(),i!=null&&(this.originalName=zy(i),this.name=By(this.originalName)),this.rank=e.length}},W7=0,El=class{constructor(t,e){this.callArgs=e,this.id=W7++,this.outboundLayer=t.outboundLayer,this.inboundLayers=t.inboundLayers,this.nodeIndices=t.nodeIndices,this.tensorIndices=t.tensorIndices,this.inputTensors=t.inputTensors,this.outputTensors=t.outputTensors,this.inputMasks=t.inputMasks,this.outputMasks=t.outputMasks,this.inputShapes=t.inputShapes,this.outputShapes=t.outputShapes;for(let n of t.inboundLayers)n!=null&&n.outboundNodes.push(this);t.outboundLayer.inboundNodes.push(this)}getConfig(){let t=[];for(let e of this.inboundLayers)e!=null?t.push(e.name):t.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:t,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},U7=0,_t=class extends J.Serializable{constructor(t={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=U7++,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 e=t.name;if(!e){let n=this.getClassName();e=Do(n)+"_"+zu(n)}if(this.name=e,this.trainable_=t.trainable==null?!0:t.trainable,t.inputShape!=null||t.batchInputShape!=null){let n;if(t.batchInputShape!=null)n=t.batchInputShape;else if(t.inputShape!=null){let s=null;t.batchSize!=null&&(s=t.batchSize),n=[s].concat(t.inputShape)}this.batchInputShape=n;let o=t.dtype;o==null&&(o=t.inputDType),o==null&&(o="float32"),this.dtype=o}t.weights!=null?this.initialWeights=t.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(t,e){return t.name+"_ib-"+e.toString()}getNodeAtIndex(t,e){if(this.inboundNodes.length===0)throw new Xr(`The layer has never been called and thus has no defined ${e}.`);if(this.inboundNodes.length<=t)throw new z(`Asked to get ${e} at node ${t}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[t]}getInputAt(t){return Tr(this.getNodeAtIndex(t,"input").inputTensors)}getOutputAt(t){return Tr(this.getNodeAtIndex(t,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new En(`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 En(`Layer ${this.name} is not connected, no input to return.`);return Tr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new En(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new En(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Tr(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(t=>t())}get updates(){return this._updates}get built(){return this._built}set built(t){this._built=t}get trainable(){return this.trainable_}set trainable(t){this._trainableWeights.forEach(e=>e.trainable=t),this.trainable_=t}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(t=>t.trainable):[]}set trainableWeights(t){this._trainableWeights=t}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(t=>!t.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(t){this._nonTrainableWeights=t}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(t){if(t=we(t),this.inputSpec==null||this.inputSpec.length===0)return;let e=we(this.inputSpec);if(t.length!==e.length)throw new z(`Layer ${this.name} expects ${e.length} inputs, but it received ${t.length} input tensors. Input received: ${t}`);for(let n=0;n<t.length;n++){let o=t[n],s=e[n];if(s==null)continue;let i=o.rank;if(s.ndim!=null&&i!==s.ndim)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${i}`);if(s.maxNDim!=null&&i>s.maxNDim)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${i}`);if(s.minNDim!=null&&i<s.minNDim)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${i}.`);if(s.dtype!=null&&o.dtype!==s.dtype)throw new z(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${o.dtype}.`);if(s.axes){let a=o.shape;for(let u in s.axes){let l=Number(u),c=s.axes[u],p=l>=0?a[l]:a[a.length+l];if(c!=null&&[c,null].indexOf(p)===-1)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${a}.`)}}if(s.shape!=null)for(let a=0;a<s.shape.length;++a){let u=s.shape[a],l=o.shape[a];if(u!=null&&l!=null&&u!==l)throw new z(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${o.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let n=we(t),o=!0;for(let i of n)if(!(i instanceof nn)){o=!1;break}let s=!0;for(let i of n)if(i instanceof nn){s=!1;break}if(o===s)throw new z("Arguments to apply() must be all SymbolicTensors or all Tensors");return hi(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let i=[];for(let a of we(t))i.push(a.shape);this.build(Tr(i)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(t),s){let i=this.call(t,e),a=we(i),u=[];for(let l of a)n.indexOf(l)!==-1&&(l=l.clone()),u.push(l);if(i=Tr(u),this.activityRegularizer!=null)throw new kt("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}else{let i=H7(t),a=this.computeOutputShape(i),u,l=q7(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?i[0]:i),a!=null&&a.length>0&&Array.isArray(a[0])?u=a.map((c,p)=>new nn(l,c,this,we(t),e,this.name,p)):u=new nn(l,a,this,we(t),e,this.name),this.addInboundNode(t,u,null,null,i,a,e),this._refCount++,this.activityRegularizer!=null)throw new kt("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return u}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape!=null)if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((n,o)=>{n!=null&&t[o]!=null&&t[o]!==n&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new En(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let n=JSON.stringify(e.outputShapes);t.indexOf(n)===-1&&t.push(n)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new En(`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 Xr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return qm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return $h(t?this.trainableWeights:this.weights)}setWeights(t){B(()=>{let e=this.weights;if(e.length!==t.length)throw new z(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let n=[],o=$h(e);for(let s=0;s<o.length;++s){let i=o[s],a=e[s],u=t[s];if(!y.arraysEqual(i.shape,u.shape))throw new z(`Layer weight shape ${i.shape} not compatible with provided weight shape ${u.shape}`);n.push([a,u])}Km(n)})}addWeight(t,e,n,o,s,i,a,u){if(this._addedWeightNames.indexOf(t)!==-1)throw new z(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(o=u!=null?u():he("zeros"));let l=o.apply(e,n),c=new Dh(l,n,t,i,a);return l.dispose(),s!=null&&this.addLoss(()=>s.apply(c.read())),i==null&&(i=!0),i?this._trainableWeights.push(c):this._nonTrainableWeights.push(c),c}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){t==null||Array.isArray(t)&&t.length===0||(t=we(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t))}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}addInboundNode(t,e,n,o,s,i,a=null){let u=we(t);e=we(e),n=we(n),o=we(o),s=Hm(s),i=Hm(i);let l=[],c=[],p=[];for(let m of u)l.push(m.sourceLayer),c.push(m.nodeIndex),p.push(m.tensorIndex);new El({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:p,inputTensors:u,outputTensors:e,inputMasks:n,outputMasks:o,inputShapes:s,outputShapes:i},a);for(let m=0;m<e.length;m++)e[m].sourceLayer=this,e[m].nodeIndex=this.inboundNodes.length-1,e[m].tensorIndex=m}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}};function H7(r){r=we(r);let t=[];for(let e of r)t.push(e.shape);return Tr(t)}function q7(r){return"float32"}function LN(r,t,e){if((t==null||e!=null&&e>0)&&(t=r.sourceLayer,e=r.nodeIndex),t.inboundNodes.length===0)return[r];{let n=t.inboundNodes[e];if(n.inboundLayers.length===0)return n.inputTensors;{let o=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],a=n.inboundLayers[s],u=n.nodeIndices[s],l=LN(i,a,u);for(let c of l)o.indexOf(c)===-1&&o.push(c)}return o}}}var xi=class extends _t{constructor(t){if(super({dtype:t.dtype,name:t.name!=null?t.name:zu("input").toString()}),t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new z("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new z("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new z("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=t.dtype||"float32";this.batchInputShape=e,this.dtype=n,this.inputSpec=[{shape:e}];let o=new nn(this.dtype,this.batchInputShape,this,[],{},this.name);o.nodeIndex=0,o.tensorIndex=0,new El({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[o],outputTensors:[o],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new z(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};xi.className="InputLayer";J.registerClass(xi);function qy(r){if(r.batchShape==null&&r.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(r.batchShape!=null&&r.shape!=null)throw new z("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=r.batchShape;r.shape!=null&&t==null&&(t=[null].concat(r.shape));let e=r.dtype;return e==null&&(e="float32"),new xi({batchInputShape:t,name:r.name,dtype:e,sparse:r.sparse}).inboundNodes[0].outputTensors[0]}function K7(r,t){if(r.dtype==null||r.dtype===t.dtype)return t;try{return Q(t,r.dtype)}catch(e){throw new z(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${r.name}' (${r.dtype}).`)}}var Oo=class{constructor(t){if(this.id2Value={},this.id2Mask=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following previous layers were accessed without issue: ${g}`);for(let N of w.outputTensors)h.push(N);g.push(I.name)}}this.nodesByDepth=m;let x=this.layers.map(b=>b.name);for(let b of x){let w=x.filter(I=>I===b).length;if(w!==1)throw new Xr(`The name "${b}" is used ${w} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(x))}this.outboundNodes=[],this.inboundNodes=[],new El({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let t={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let e of this.layers)t.numDisposedVariables+=e.dispose().numDisposedVariables;for(let e of this.internalContainerRefs)t.numDisposedVariables+=e.dispose().numDisposedVariables}return t.refCountAfterDispose=this._refCount,t}get trainable(){return this.trainable_}set trainable(t){this.layers.forEach(e=>{e._trainableWeights.forEach(n=>n.trainable=t)}),this.trainable_=t}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("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 t=[];for(let e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.layers)e.push(...n.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){let n={},o=0;for(let i of this.layers)for(let a of i.weights){if(n[a.originalName]!=null)throw new z(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,o++}let s=[];for(let i in t){let a=i;if(n[i]==null){let u=i.split("/");a=u.slice(0,-2).concat([u[u.length-1]]).join("/")}if(n[a]!=null)s.push([n[a],t[i]]);else if(e)throw new z(`Provided weight data has no target variable: ${i}`);delete n[a]}if(e){let i=[];for(let a in n)i.push(a);if(i.length>0)throw new z(`${i.length} of ${o} weights are not set: ${i}`)}Km(s)}updatedConfig(){let t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion=`tfjs-layers ${ef}`,e.backend="TensorFlow.js",e}toJSON(t,e=!0){let n=ib(this.updatedConfig());return e?JSON.stringify(n):n}call(t,e){return B(()=>{t=we(t);let n=new Oo;for(let o=0;o<this.inputs.length;++o)n.add(this.inputs[o],t[o]);return jc(this.outputs,n,e)})}computeMask(t,e){return B(()=>{t=we(t);let n;return e==null?n=Ao(null,t.length):n=we(e),this.runInternalGraph(t,n)[1]})}computeOutputShape(t){let e=Hm(t);if(e.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let a=0;a<e.length;a++){let u=this.inputLayers[a],l=e[a],c=u.name+"_0_0";n[c]=l}let o=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(_h);if(o.length>1)for(let a of o){let u=this.nodesByDepth[a];for(let l of u){let c=l.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<l.inboundLayers.length;h++){let g=l.inboundLayers[h],x=l.nodeIndices[h],b=l.tensorIndices[h],w=`${g.name}_${x}_${b}`,I=n[w];p.push(I)}let m=c.computeOutputShape(Tr(p)),f=Hm(m),d=c.inboundNodes.indexOf(l);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;n[g]=f[h]}}}let s=[],i=[];for(let a=0;a<this.outputLayers.length;a++){let u=this.outputLayers[a],l=this.outputLayersNodeIndices[a],c=this.outputLayersTensorIndices[a],p=`${u.name}_${l}_${c}`;i.push(p)}for(let a=0;a<i.length;a++){let u=i[a];fo(u in n),s.push(n[u])}return Tr(s)}runInternalGraph(t,e){e==null&&(e=Ao(null,t.length));let n={};for(let u=0;u<this.inputs.length;++u){let l=this.inputs[u],c=t[u],p=e[u];n[l.id]=[c,p]}let o=Object.keys(this.nodesByDepth).map(u=>parseInt(u,10)).sort(_h);for(let u of o){let l=this.nodesByDepth[u];for(let c of l){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in n&&d.push(n[h.id]);if(d.length===m.length){let h={},g,x,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[I,N]=d[0];h.mask==null&&(h.mask=N),b=we(p.call(I,h)),w=we(p.computeMask(I,N)),g=[I],x=[N]}else g=d.map(I=>I[0]),x=d.map(I=>I[1]),h.mask==null&&(h.mask=x),b=we(p.call(g,h)),w=we(p.computeMask(g,x));if(p.activityRegularizer)throw new kt("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let I=0;I<f.length;++I){let N=f[I],E=b[I],A=w[I];n[N.id]=[E,A]}}}}let s=[],i=[],a=[];for(let u of this.outputs){fo(u.id in n,`Could not compute output ${u.name} : ${u.id}`);let[l,c]=n[u.id];a.push(l.shape),s.push(l),i.push(c)}return[s,i,a]}buildNodeConversionMap(t){let e={},n;for(let o of this.layers){n=o instanceof Kn?1:0;for(let s=0;s<o.inboundNodes.length;s++){let i=Kn.nodeKey(o,s);this.containerNodes.has(i)&&(e[i]=n,n+=1)}}return e}getLayer(t,e){if(e!=null)return this.findLayer(e);if(t==null)throw new z("Provide either a layer name or layer index");if(typeof t=="number")return this.findLayer(t);for(let n of this.layers)if(n.name===t)return n;throw new z(`No such layer: ${t}`)}findLayer(t){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}calculateLosses(){return B(()=>{let t=[];for(let e of this.layers)for(let n=0;n<e.inboundNodes.length;++n){let o=Kn.nodeKey(e,n);this.containerNodes.has(o)&&t.push(...e.calculateLosses())}return t})}getConfig(){let t={name:this.name},e=this.buildNodeConversionMap(this.layers),n=[];for(let i of this.layers){let a=i.getClassName(),u=i.getConfig(),l=[];for(let p=0;p<i.inboundNodes.length;p++){let m=i.inboundNodes[p],f=Kn.nodeKey(i,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${i.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],b=m.nodeIndices[g],w=m.tensorIndices[g],I=Kn.nodeKey(x,b),N=e[I];N==null&&(N=0),h.push([x.name,N,w,d])}l.push(h)}}}let c={};c.name=i.name,c.className=a,c.config=u,c.inboundNodes=l,n.push(c)}t.layers=n;let o=[];for(let i=0;i<this.inputLayers.length;i++){let a=this.inputLayers[i],u=this.inputLayersNodeIndices[i],l=Kn.nodeKey(a,u);if(!this.containerNodes.has(l))continue;let c=e[l];c==null&&(c=0);let p=this.inputLayersTensorIndices[i];o.push([a.name,c,p])}t.inputLayers=o;let s=[];for(let i=0;i<this.outputLayers.length;i++){let a=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=Kn.nodeKey(a,u);if(!this.containerNodes.has(l))continue;let c=e[l];c==null&&(c=0);let p=this.outputLayersTensorIndices[i];s.push([a.name,c,p])}return t.outputLayers=s,t}static fromConfig(t,e,n={},o=!1){let s={},i={};function a(g,x){g.name in i?i[g.name].push(x):i[g.name]=[x]}function u(g,x){let b=[],w;for(let I of x){let N=I[0],E=I[1],A=I[2];if(w=I[3]==null?{}:I[3],!(N in s)){a(g,x);return}let D=s[N];if(D.inboundNodes.length<=E){a(g,x);return}let F=D.inboundNodes[E];b.push(F.outputTensors[A])}b.length>0&&g.apply(Tr(b),w)}function l(g){let x=g.name,b=Cn(g,e.customObjects!=null?e.customObjects:{});b.setFastWeightInitDuringBuild(o),s[x]=b,g.inboundNodes.forEach(I=>{if(!(I instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${I}`);a(b,I)})}let c=e.name,p=e.layers;for(let g of p)l(g);for(;!z$(i);)for(let g of p){let x=s[g.name];if(x.name in i){let b=i[x.name];delete i[x.name];for(let w of b)u(x,w)}}let m=[],f=[],d=e.inputLayers;for(let g of d){let x=g[0],b=g[1],w=g[2];fo(x in s);let N=s[x].inboundNodes[b].outputTensors;m.push(N[w])}let h=e.outputLayers;for(let g of h){let x=g[0],b=g[1],w=g[2];fo(x in s);let N=s[x].inboundNodes[b].outputTensors;f.push(N[w])}return new t({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new z("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(let t of this.layers)if(t.stateful)return!0;return!1}resetStates(){B(()=>{this.layers.forEach(t=>{t.stateful&&t.resetStates()})})}};function BZ(r,t,e){let n=t.length;if(r==null||Array.isArray(r)&&r.length===0)return t.map(o=>null);if(n===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&t[0]in r?[r[t[0]]]:[r];if(Array.isArray(r)){if(r.length!==n)throw new Error(`Provided ${e} is an array of ${r.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let o=[];return t.forEach(s=>{s in r?o.push(r[s]):o.push(null)}),o}else throw new Error(`The model has multiple (${n}) outputs, so ${e} must be either an array with ${n} elements or an object with ${t} keys. Provided ${e} not understood: ${JSON.stringify(r)}`)}function ab(r,t){return BZ(r,t,"classWeight")}async function lb(r,t,e,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let o=B(()=>{if(r.shape.length===1)return cn(r);if(r.shape.length===2){if(r.shape[1]>1)return oa(r,1);if(r.shape[1]===1)return R(r,[r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());Tt(o);let i=[];return s.forEach(a=>{if(e[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`);i.push(e[a])}),Ke(i,"float32")}else return null}function SR(r,t){return $(r,t)}var VZ=32;function TR(r,t){let e,n,o=t;e=o.xs,n=o.ys,y.assert(e!=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}`);let s=NR("input",r.inputNames,e),i=NR("output",r.outputNames,n),a=s[0].shape[0];y.assert(s.length===r.inputs.length,()=>`LayersModel has ${r.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(r.inputNames)})`),y.assert(i.length===r.outputs.length,()=>`LayersModel has ${r.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let u=0;u<s.length;u++)y.assert(s[u].shape[0]===a,()=>`Batch size mismatch: input ${r.inputNames[u]} has ${s[u].shape[0]}; expected  ${a} based on input ${r.inputNames[0]}.`);for(let u=0;u<i.length;u++)y.assert(i[u].shape[0]===a,()=>`Batch size mismatch: output ${r.outputNames[u]} has ${i[u].shape[0]}; expected  ${a} based on input ${r.inputNames[0]}.`);return{xs:s,ys:i}}function NR(r,t,e){if(e instanceof Ot)return[e];if(Array.isArray(e))return y.assert(e.length===t.length,()=>`Received an array of ${e.length} Tensors, but expected ${t.length} to match the ${r} keys ${t}.`),e;{let n=[];for(let o of t){if(e[o]==null)throw new z(`The feature data generated by the dataset lacks the required ${r} key '${o}'.`);n.push(e[o])}return n}}function GZ(r){if(r.length===3)throw new kt("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function _R(r,t,e){let n=e.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(e!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(e.epochs!=null&&e.epochs>0&&Number.isInteger(e.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${e.epochs}`),y.assert(!n||e.batchesPerEpoch>0&&Number.isInteger(e.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${e.batchesPerEpoch}`),y.assert(e.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let o=e.validationData!=null,s,i;if(o)if(kR(e.validationData))y.assert(e.validationBatches==null||e.validationBatches>0&&Number.isInteger(e.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${e.validationBatches}`);else{let g=GZ(e.validationData);s=g.xs,i=g.ys}let a=r.makeTrainFunction(),u=r.getDedupedMetricsNames(),l;o?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=Qy(e.callbacks,e.yieldEvery),p=e.verbose==null?1:e.verbose,{callbackList:m,history:f}=tb(c,p,e.epochs,null,null,WZ(t,e),null,o,l);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=e.initialEpoch==null?0:e.initialEpoch,h=await t.iterator();for(;d<e.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(n||(h=await t.iterator());!n||x<e.batchesPerEpoch;){let w=await h.next();if(n&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${e.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${e.batchesPerEpoch*e.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(w.value!=null){let{xs:I,ys:N}=TR(r,w.value),E={};E.batch=b,E.size=I[0].shape[0],await m.onBatchBegin(b,E);let A=[];if(e.classWeight!=null){let P=ab(e.classWeight,r.outputNames);for(let V=0;V<P.length;++V)A.push(await lb(N[V],null,P[V]))}let D=I.concat(N).concat(A),F=a(D);Tt(D);for(let P=0;P<u.length;++P){let V=u[P],G=F[P];E[V]=G,$e(G)}await m.onBatchEnd(b,E),Xy(E),b++,x++}if(n?x>=e.batchesPerEpoch:w.done){if(o){let I;kR(e.validationData)?I=we(await r.evaluateDataset(e.validationData,{batches:e.validationBatches})):I=we(r.evaluate(s,i,{batchSize:e.validationBatchSize==null?VZ:e.validationBatchSize,verbose:0}));for(let N=0;N<r.metricsNames.length;++N)g[`val_${r.metricsNames[N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egorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(n.indexOf(i)!==-1){let u=s.shape.slice(1),l=a.slice(1);for(let c=0;c<u.length;++c){let p=u[c],m=l[c];if(m!=null&&p!==m)throw new z(`A target Tensor with shape ${s.shape} was passed for an output of shape ${a}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function $R(r,t,e,n=!0,o=""){let s;if(Array.isArray(r)){if(r.length!==t.length)throw new z(`Error when checking model ${o}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${r.length} Tensors(s).`);s=r}else{if(t.length>1)throw new z(`The model expects ${t.length} ${o} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(r.shape)}.`);s=[r]}if(e!=null)for(let i=0;i<t.length;++i){if(e[i]==null)continue;let a=s[i];if(a.shape.length!==e[i].length)throw new z(`Error when checking ${o}: expected ${t[i]} to have ${e[i].length} dimension(s), but got array with shape ${JSON.stringify(a.shape)}`);for(let u=0;u<e[i].length;++u){if(u===0&&!n)continue;let l=a.shape[u],c=e[i][u];if(c!=null&&c!==l)throw new z(`Error when checking ${o}: expected ${t[i]} to have shape ${JSON.stringify(e[i])} but got array with shape ${JSON.stringify(a.shape)}.`)}}}function jZ(r,t){if(r==null||Array.isArray(r)&&r.length===0)return t.map(n=>[]);let e;if(typeof r=="string"||typeof r=="function")e=[r];else if(Array.isArray(r)||typeof r=="object")e=r;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${r}`);if(Array.isArray(e))return t.map(n=>e);{let n=[];for(let o of t){let s=e.hasOwnProperty(o)?e[o]:[];Array.isArray(s)||(s=[s]),n.push(s)}return n}}var XZ="layers-model",jn=class extends Kn{constructor(t){super(t),this.isTraining=!1}summary(t,e,n=console.log){if(!this.built)throw new z("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");CR(this,t,e,n)}compile(t){if(t.loss==null&&(t.loss=[]),this.loss=t.loss,typeof t.optimizer=="string")this.optimizer_=IR(t.optimizer),this.isOptimizerOwned=!0;else{if(!(t.optimizer instanceof Kr))throw new z("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=t.optimizer,this.isOptimizerOwned=!1}let e=[];if(!Array.isArray(t.loss)&&typeof t.loss!="string"&&typeof t.loss!="function"){t.loss=t.loss;for(let i in t.loss)if(this.outputNames.indexOf(i)===-1)throw new z(`Unknown entry in loss dictionary: "${i}". Only expected the following keys: ${this.outputNames}`);for(let i of this.outputNames)t.loss[i]==null&&console.warn(`Output "${i}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${i} during training`),e.push(eb(t.loss[i]))}else if(Array.isArray(t.loss)){if(t.loss.length!==this.outputs.length)throw new z(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${t.loss}.`);e=t.loss.map(a=>eb(a))}else{let i=eb(t.loss);this.outputs.forEach(a=>{e.push(i)})}this.lossFunctions=e,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let a=this.internalOutputShapes[i],u=this.outputNames[i];this.feedOutputNames.push(u),this.feedOutputShapes.push(a),this.feedLossFns.push(this.lossFunctions[i])}let n=[];this.metrics=t.metrics,this.metricsNames=["loss"],this.metricsTensors=[],hi("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(n.indexOf(i)!==-1)continue;let a=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([a,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let o=jZ(t.metrics,this.outputNames),s=(i,a,u)=>{this.outputNames.length>1&&(a=this.outputNames[i]+"_"+a),this.metricsNames.push(a),this.metricsTensors.push([u,i])};hi("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(n.indexOf(i)!==-1)continue;let a=o[i];(l=>{let c="",p,m,f;for(let d of l){if(typeof d=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(d)!==-1){let g=this.internalOutputShapes[i];g[g.length-1]===1||this.lossFunctions[i]===tf?["accuracy","acc"].indexOf(d)!==-1?m=Ph:["crossentropy","ce"].indexOf(d)!==-1&&(m=nb):this.lossFunctions[i]===Qm?["accuracy","acc"].indexOf(d)!==-1?m=ob:["crossentropy","ce"].indexOf(d)!==-1&&(m=GN):["accuracy","acc"].indexOf(d)!==-1?m=Mh:["crossentropy","ce"].indexOf(d)!==-1&&(m=Lh);let x;["accuracy","acc"].indexOf(d)!==-1?x="acc":["crossentropy","ce"].indexOf(d)!==-1&&(x="ce"),f=m,p=c+x}else f=bR(d),p=c+zh(d);let h;hi(p,()=>{h=f}),s(i,p,h)}})(a)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(t,e,n={}){let o=n.batchSize==null?32:n.batchSize;ub(o);let s=!0,i=this.standardizeUserDataXY(t,e,s,o);try{let a=i[0].concat(i[1]);this.makeTestFunction();let u=this.testFunction,l=this.testLoop(u,a,o,n.verbose,n.steps);return Tr(l)}finally{Po(i[0],t),Po(i[1],e)}}async evaluateDataset(t,e){return this.makeTestFunction(),ER(this,t,e)}checkNumSamples(t,e,n,o="steps"){let s;if(n!=null){if(s=null,e!=null)throw new z(`If ${o} is set, batchSize must be null or undefined.Got batchSize = ${e}`)}else if(t!=null)Array.isArray(t)?s=t[0].shape[0]:s=t.shape[0];else throw new z(`Either the input data should have a defined shape, or ${o} shoud be specified.`);return s}execute(t,e){if(Array.isArray(e)&&e.length===0)throw new z("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(e),o=n?e:[e],s=this.retrieveSymbolicTensors(o),i=new Oo;if(t instanceof Ot&&(t=[t]),Array.isArray(t)){if(t.length!==this.inputs.length)throw new z(`The number of inputs provided (${t.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let u=0;u<this.inputs.length;++u)i.add(this.inputs[u],t[u])}else for(let u of this.inputs){let l=t[u.name];if(l==null)throw new z(`No value is provided for the model's input ${u.name}`);i.add(u,l)}let a=jc(s,i);return n?a:a[0]}retrieveSymbolicTensors(t){let e=Ao(null,t.length),n=t.length;for(let o of this.layers){let s=Array.isArray(o.output)?o.output:[o.output],i=s.map(a=>a.name);for(let a=0;a<t.length;++a){let u=i.indexOf(t[a]);if(u!==-1&&(e[a]=s[u],n--),n===0)break}if(n===0)break}if(n>0){let o=[];throw e.forEach((s,i)=>{s==null&&o.push(t[i])}),new z(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(o)}`)}return e}predictLoop(t,e=32,n=!1){return B(()=>{let o=this.checkNumSamples(t);if(n)throw new kt("Verbose predictLoop() is not implemented yet.");let s=pb(o,e),i=this.outputs.map(a=>[]);for(let a=0;a<s.length;++a)B(()=>{let l=s[a][0],c=s[a][1],p=rf(t,l,c),m=[];if(Array.isArray(p))for(let d=0;d<p.length;++d)m.push({key:this.inputs[d],value:p[d]});else m.push({key:this.inputs[0],value:p});let f=new Oo(m);return jc(this.outputs,f)}).forEach((l,c)=>i[c].push(l));return Tr(i.map(a=>ie(a,0)))})}predict(t,e={}){let n=HN(t);$R(n,this.inputNames,this.feedInputShapes,!1);try{let o=e.batchSize==null?32:e.batchSize;return ub(o),this.predictLoop(n,o)}finally{Po(n,t)}}predictOnBatch(t){$R(t,this.inputNames,this.feedInputShapes,!0);let e=(Array.isArray(t)?t[0]:t).shape[0];return this.predictLoop(t,e)}standardizeUserDataXY(t,e,n=!0,o){if(this.optimizer_==null)throw new Xr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let s=[];for(let i=0;i<this.feedOutputShapes.length;++i){let a=this.feedOutputShapes[i];this.feedLossFns[i]===Qm?s.push(a.slice(0,a.length-1).concat([1])):s.push(a)}if(t=DR(t,this.feedInputNames,this.feedInputShapes,!1,"input"),e=DR(e,this.feedOutputNames,s,!1,"target"),qZ(t,e,null),KZ(e,this.feedLossFns,this.feedOutputShapes),this.stateful&&o!=null&&o>0&&t[0].shape[0]%o!==0)throw new z(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${o}. Found: ${t[0].shape[0]} sample(s).`);return[t,e]}async standardizeUserData(t,e,n,o,s=!0,i){let[a,u]=this.standardizeUserDataXY(t,e,s,i);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(o!=null){let c=ab(o,this.outputNames);l=[];for(let p=0;p<c.length;++p)l.push(await lb(u[p],null,c[p]))}return[a,u,l]}testLoop(t,e,n,o=0,s){return B(()=>{let i=this.checkNumSamples(e,n,s,"steps"),a=[];if(o>0)throw new kt("Verbose mode is not implemented yet.");if(s!=null)throw new kt("steps mode in testLoop() is not implemented yet");{let u=pb(i,n),l=Ke(xn(0,i));for(let c=0;c<u.length;++c){let p=u[c][0],m=u[c][1],f=Tl(l,p,m-p),d=cb(e,f),h=t(d);if(c===0)for(let g=0;g<h.length;++g)a.push(ft(0));for(let g=0;g<h.length;++g){let x=h[g];a[g]=Y(a[g],$(m-p,x))}}for(let c=0;c<a.length;++c)a[c]=ct(a[c],i)}return a})}getDedupedMetricsNames(){let t=this.metricsNames,e=[];for(let n=0;n<t.length;++n){let o=t[n],s=o;if($N(t,o)>1){let i=$N(t.slice(0,n),o);s+=`_${i}`}e.push(s)}return e}makeTrainFunction(){return t=>{let e=[],n=t.slice(0,this.inputs.length),o=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=t.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),i=[],a=()=>{let p=[];for(let h=0;h<this.inputs.length;++h)p.push({key:this.inputs[h],value:n[h]});let m=new Oo(p),f=jc(this.outputs,m,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let g=this.lossFunctions[h],x=g(o[h],f[h]);s[h]!=null&&(x=SR(x,s[h]));let b=ke(x);e.push(b),h===0?d=x:d=Y(d,x)}for(let h=0;h<this.metricsTensors.length;++h){let g;if(this.outputs.length>1&&h<this.outputs.length)g=e[h];else{let x=this.metricsTensors[h][0],b=this.metricsTensors[h][1];g=ke(x(o[b],f[b]))}$e(g),i.push(g)}return d=ke(d),this.calculateLosses().forEach(h=>{d=Y(d,h)}),d},u=this.collectedTrainableWeights.map(p=>p.read()),l=!0;return[this.optimizer_.minimize(a,l,u)].concat(i)}}makeTestFunction(){this.testFunction=t=>B(()=>{let e=[],n,o=t.slice(0,this.inputs.length),s=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=[];for(let l=0;l<this.inputs.length;++l)i.push({key:this.inputs[l],value:o[l]});let a=new Oo(i),u=jc(this.outputs,a);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],p=ke(c(s[l],u[l]));l===0?n=p:n=Y(n,p),e.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],p=this.metricsTensors[l][1],m=ke(c(s[p],u[p]));e.push(m)}return e})}async fit(t,e,n={}){if(this.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");this.isTraining=!0;let o,s,i,a,u,l,c,p,m;try{let f=n.batchSize==null?32:n.batchSize;ub(f);let d=!1,h=await this.standardizeUserData(t,e,n.sampleWeight,n.classWeight,d,f);o=h[0],s=h[1],m=h[2];let g=!1,x;if(n.validationData!=null&&n.validationData.length>0){if(g=!0,n.validationData.length===2)u=n.validationData[0],l=n.validationData[1];else throw n.validationData.length===3?new kt("validationData including sample weights is not supported yet."):new z(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${n.validationData} is invalid.`);let F=!0,P=await this.standardizeUserData(u,l,null,null,F,f);c=P[0],p=P[1],x=c.concat(p)}else if(n.validationSplit!=null&&n.validationSplit>0&&n.validationSplit<1){g=!0;let F=Math.floor(o[0].shape[0]*(1-n.validationSplit)),P=o[0].shape[0];c=rf(o,F,P),i=o,o=rf(o,0,F),p=rf(s,F,P),a=s,s=rf(s,0,F),x=c.concat(p)}else n.validationSteps!=null&&(g=!0);let b=o.concat(s).concat(m);this.checkTrainableWeightsConsistency();let w=this.makeTrainFunction(),I=this.getDedupedMetricsNames(),N,E;g?(this.makeTestFunction(),N=this.testFunction,E=I.slice().concat(I.map(F=>"val_"+F))):(N=null,x=[],E=I.slice());let A=Qy(n.callbacks,n.yieldEvery);return await this.fitLoop(w,b,I,f,n.epochs,n.verbose,A,N,x,n.shuffle,E,n.initialEpoch,null,null)}finally{this.isTraining=!1,Po(o,t),Po(s,e),Po(i,t),Po(a,e),Po(c,u),Po(p,l),m!=null&&Tt(m)}}async fitLoop(t,e,n,o,s,i,a,u,l,c,p,m,f,d){o==null&&(o=32),s==null&&(s=1),c==null&&(c=!0),m==null&&(m=0);let h=!1;if(u!=null&&l!=null&&(h=!0),d!=null&&(h=!0,f==null))throw new z("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=this.checkNumSamples(e,o,f,"steps_per_epoch"),x;g!=null&&(x=xn(0,g)),i==null&&(i=1);let{callbackList:b,history:w}=tb(a,i,s,m,g,f,o,h,p);b.setModel(this),this.history=w,await b.onTrainBegin(),this.stopTraining_=!1;for(let I=m;I<s;++I){await b.onEpochBegin(I);let N={};if(f!=null)throw new kt("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new kt("batch shuffling is not implemneted yet");c&&y.shuffle(x);let E=Ke(x),A=pb(g,o);for(let D=0;D<A.length;++D){let F={};if(await b.onBatchBegin(D,F),B(()=>{let P=A[D][0],V=A[D][1],G=Tl(E,P,V-P);F.batch=D,F.size=V-P;let W=cb(e,G),q=t(W);for(let H=0;H<n.length;++H){let K=n[H],X=q[H];F[K]=X,$e(X)}if(D===A.length-1&&h){let H=this.testLoop(u,l,o);for(let K=0;K<n.length;++K){let X=n[K],Z=H[K];$e(Z),N["val_"+X]=Z}}}),await b.onBatchEnd(D,F),Xy(F),this.stopTraining_)break}E.dispose()}if(await b.onEpochEnd(I,N),this.stopTraining_)break}return await b.onTrainEnd(),await this.history.syncData(),this.history}async fitDataset(t,e){return _R(this,t,e)}async trainOnBatch(t,e){let n=await this.standardizeUserData(t,e),o=n[0],s=n[1],a=this.makeTrainFunction()(o.concat(s)),u=[];for(let l of a){let c=await l.data();u.push(c[0])}return Tt(a),Po(n[0],t),Po(n[1],e),Tr(u)}getNamedWeights(t){let e=[],n=t!=null&&t.trainableOnly,o=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let i=0;i<o.length;++i)n&&!o[i].trainable||e.push({name:o[i].originalName,tensor:s[i]});return e}set stopTraining(t){this.stopTraining_=t}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)}dispose(){let t=super.dispose();if(t.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let e=fh().numTensors;this.optimizer_.dispose(),t.numDisposedVariables+=e-fh().numTensors}return t}getLossIdentifiers(){let t;if(typeof this.loss=="string")t=Do(this.loss);else if(Array.isArray(this.loss)){for(let e of this.loss)if(typeof e!="string")throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map(e=>Do(e))}else{let e=Object.keys(this.loss);t={};let n=this.loss;for(let o of e)if(typeof n[o]=="string")t[o]=Do(n[o]);else throw new Error("Serialization of non-string loss is not supported.")}return t}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Do(zh(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(t=>Do(zh(t)));{let t={};for(let e in this.metrics)t[e]=Do(zh(this.metrics[e]));return t}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(t){if(t.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(t.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(t.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let e=Zc(t.optimizer_config),n=Cn(e),o;if(typeof t.loss=="string")o=kl(t.loss);else if(Array.isArray(t.loss))o=t.loss.map(i=>kl(i));else if(t.loss!=null){o={};for(let i in t.loss)o[i]=kl(t.loss[i])}let s;if(Array.isArray(t.metrics))s=t.metrics.map(i=>kl(i));else if(t.metrics!=null){s={};for(let i in t.metrics)s[i]=kl(t.metrics[i])}this.compile({loss:o,metrics:s,optimizer:n})}async save(t,e){if(typeof t=="string"){let l=Lr.getSaveHandlers(t);if(l.length===0)throw new z(`Cannot find any save handlers for URL '${t}'`);if(l.length>1)throw new z(`Found more than one (${l.length}) save handlers for URL '${t}'`);t=l[0]}if(t.save==null)throw new z("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Lr.encodeWeights(this.getNamedWeights(e)),o=!1,s=null,a={modelTopology:this.toJSON(s,o),format:XZ,generatedBy:`TensorFlow.js tfjs-layers v${ef}`,convertedBy:null};if((e==null?!1:e.includeOptimizer)&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:p}=await Lr.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...p),n.data=Lr.concatenateArrayBuffers([n.data,c])}return this.userDefinedMetadata!=null&&(UN(this.userDefinedMetadata,this.name,!0),a.userDefinedMetadata=this.userDefinedMetadata),a.weightData=n.data,a.weightSpecs=n.specs,t.save(a)}setUserDefinedMetadata(t){UN(t,this.name),this.userDefinedMetadata=t}getUserDefinedMetadata(){return this.userDefinedMetadata}};jn.className="Model";J.registerClass(jn);var mb=class extends jn{};mb.className="Functional";J.registerClass(mb);async function RR(r,t){"modelTopology"in r||(r={modelTopology:r}),r=r;let e=r.modelTopology;e.model_config!=null&&(e=e.model_config);let n=Zc(e),o=Cn(n,t);if(r.weightsManifest!=null){let s=await Lr.loadWeights(r.weightsManifest,r.pathPrefix,o.weights.map(a=>a.originalName)),i={};for(let a of o.weights)i[a.originalName]=s[a.originalName];o.loadWeights(i),Tt(s)}return o}async function FR(r,t){if(t==null&&(t={}),typeof r=="string"){let e=Lr.getLoadHandlers(r,t);if(e.length===0)e.push(Lr.browserHTTPRequest(r,t));else if(e.length>1)throw new z(`Found more than one (${e.length}) load handlers for URL '${r}'`);r=e[0]}return YZ(r,void 0,t)}async function YZ(r,t,e){if(e==null&&(e={}),r.load==null)throw new z("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await r.load(),o=n.modelTopology;o.model_config!=null&&(o=o.model_config);let s=e.strict==null?!0:e.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,a=Cn(Zc(o),t,i),u=n.trainingConfig;if(u!=null&&a.loadTrainingConfig(u),n.userDefinedMetadata!=null&&a.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new z("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=ZZ(n.weightData,n.weightSpecs);a.loadWeights(l,s),a.optimizer!=null&&c.length>0&&await a.optimizer.setWeights(c),Tt(l),Tt(c.map(p=>p.tensor))}return a}function ZZ(r,t){let e=Lr.decodeWeights(r,t),n={},o=[];return t.forEach(s=>{s.group==="optimizer"?o.push({name:s.name,tensor:e[s.name]}):n[s.name]=e[s.name]}),{modelWeights:n,optimizerWeights:o}}var Ia=class extends jn{constructor(t){if(super({inputs:[],outputs:[]}),t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:zu("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){if(t.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new z(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof Ia||t instanceof jn,n;if(e){if(n=t,n.outputs.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new z("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(t.inboundNodes.length===0){if(t.batchInputShape==null)throw new z("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let o=qy({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(o)}if(e)this.outputs=n.outputs,this.inputs=n.inputs;else{if(t.inboundNodes.length!==1)throw new z(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${t.name} which has ${t.inboundNodes.length} pre-existing inbound connections.`);if(t.inboundNodes[0].outputTensors.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=LN(this.outputs[0])}this.inboundNodes=[],new El({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Ao(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(o=>o.shape),outputShapes:this.outputs[0].shape})}else{let o=t.apply(this.outputs[0]);if(Array.isArray(o))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[o],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(t,e){return this.model==null&&this.build(),this.model.call(t,e)}build(t){if(Gt(t),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new jn({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(t,e,n=console.log){this.built||this.build(),super.summary(t,e,n)}setWeights(t){this.model==null&&this.build(),this.model.setWeights(t)}evaluate(t,e,n={}){if(!this.built)throw new Xr("The model needs to be compiled before being used.");return this.model.evaluate(t,e,n)}async evaluateDataset(t,e){if(!this.built)throw new Xr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,e,n={}){if(!this.built)throw new Xr("The model needs to be compiled before being used.");return this.model.fit(t,e,n)}async fitDataset(t,e){if(!this.built)throw new Xr("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=e}else y.assert(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof Ia))throw new kt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=Cn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Ia.className="Sequential";J.registerClass(Ia);function JZ(r){return new jn(r)}function QZ(r){return new Ia(r)}function KN(r){return qy(r)}function tJ(r,t){In.registerCallbackConstructor(r,t)}var on=class extends J.Serializable{getConfig(){return{}}},fb=class extends on{apply(t,e=1){return tR(t,e)}};fb.className="elu";J.registerClass(fb);var db=class extends on{apply(t){return Im(t)}};db.className="selu";J.registerClass(db);var hb=class extends on{apply(t){return Mr(t)}};hb.className="relu";J.registerClass(hb);var gb=class extends on{apply(t){return B(()=>mo(6,Mr(t)))}};gb.className="relu6";J.registerClass(gb);var xb=class extends on{apply(t){return t}};xb.className="linear";J.registerClass(xb);var yb=class extends on{apply(t){return en(t)}};yb.className="sigmoid";J.registerClass(yb);var bb=class extends on{apply(t){return rR(t)}};bb.className="hardSigmoid";J.registerClass(bb);var wb=class extends on{apply(t){return pi(t)}};wb.className="softplus";J.registerClass(wb);var Ib=class extends on{apply(t){return eR(t)}};Ib.className="softsign";J.registerClass(Ib);var Cb=class extends on{apply(t){return ia(t)}};Cb.className="tanh";J.registerClass(Cb);var nf=class extends on{apply(t,e=-1){return Fu(t,e)}};nf.className="softmax";J.registerClass(nf);var vb=class extends on{apply(t,e=-1){return hm(t,e)}};vb.className="logSoftmax";J.registerClass(vb);var Sb=class extends on{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Sb.className="swish";J.registerClass(Sb);var Nb=class extends on{apply(t){return B(()=>$(t,ia(pi(t))))}};Nb.className="mish";J.registerClass(Nb);function yi(r){return r.getClassName()}function jN(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,"activation")}function bi(r){if(r==null){let t={};return t.className="linear",t.config={},jN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},jN(t)}else return r instanceof on?r:jN(r)}function XN(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends J.Serializable{},Wu=class extends kb{constructor(t){super(),XN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Vc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Wu.className="L1L2";J.registerClass(Wu);function MR(r){return XN(r),new Wu({l1:r!=null?r.l1:null,l2:0})}function LR(r){return XN(r),new Wu({l2:r!=null?r.l2:null,l1:0})}var OR={l1l2:"L1L2"};function me(r){return Fm(r)}function PR(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ce(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in OR?OR[r]:r,config:{}};return PR(e)}else return r instanceof kb?r:PR(r)}var of=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Mr(t);return this.maxValue!=null&&(n=Sr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};of.className="ReLU";J.registerClass(of);var sf=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return _u(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};sf.className="LeakyReLU";J.registerClass(sf);var af=class extends _t{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=he(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o<t.length;++o)n[o]=t[o];this.inputSpec=[new Ie({ndim:t.length,axes:n})],this.built=!0}call(t,e){return t=St(t),Ru(t,this.alpha.read())}getConfig(){let t={alphaInitializer:_e(this.alphaInitializer),alphaRegularizer:me(this.alphaRegularizer),alphaConstraint:Be(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}};af.className="PReLU";J.registerClass(af);var lf=class extends _t{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA=1,t==null&&(t={}),t.alpha!=null&&t.alpha!==this.DEFAULT_ALPHA)throw new kt(`Non-default alpha value (${t.alpha}) is not supported by the ELU layer yet.`);this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return ca(n)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};lf.className="ELU";J.registerClass(lf);var uf=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_THETA=1,t==null&&(t={}),this.theta=t.theta==null?this.DEFAULT_THETA:t.theta}call(t,e){let n=St(t);return $(n,Q(Fe(n,this.theta),"float32"))}computeOutputShape(t){return t}getConfig(){let t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}};uf.className="ThresholdedReLU";J.registerClass(uf);var cf=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_AXIS=1,t==null&&(t={}),this.softmax=new nf().apply,this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis}call(t,e){let n=St(t);return this.softmax(n,this.axis)}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};cf.className="Softmax";J.registerClass(cf);function Uu(r,t,e){if(typeof r=="number")return Ao(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${r.length} elements.`);for(let n=0;n<t;++n){let o=r[n];if(!Y$(o))throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function An(r,t,e,n,o=1){if(r==null)return r;let s=t+(t-1)*(o-1),i;return e==="same"?i=r:i=r-s+1,Math.floor((i+n-1)/n)}function wi(r,t,e,n){if(r==null)return null;if(n==="valid")r=r*t+gi([e-t,0]);else if(n==="same")r=r*t;else throw new z(`Unsupport padding mode: ${n}.`);return r}function Bh(r,t){return B(()=>(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,1]):r))}function YN(r,t){return B(()=>(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,4,1]):r))}function rJ(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=yn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(r=Vt(r,[0,2,1])),o==="causal")throw new kt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=cm(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=bn(a,e)),a})}function zR(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=yn()),Oe(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Bh(r,s);if(o==="causal")throw new kt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Lu.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function nJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=yn()),Oe(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=YN(r,s);if(o==="causal")throw new kt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Rx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=bn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Jc=class extends _t{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new kt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Uu(e.kernelSize,t,"kernelSize"),this.strides=Uu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,gn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Oe(this.dataFormat),this.activation=bi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=he(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=Uu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(fo("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:yi(this.activation),useBias:this.useBias,biasInitializer:_e(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Hu=class extends Jc{constructor(t,e){super(t,e),this.kernel=null,Hu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,"filters"),this.kernelInitializer=he(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Py(this.activation.getClassName());if(s!=null&&this.rank===2)n=zR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=rJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=zR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new kt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<n.length;++s){let i=An(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(i)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:_e(this.kernelInitializer),kernelRegularizer:me(this.kernelRegularizer),kernelConstraint:Be(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}},Dl=class extends Hu{constructor(t){super(2,t),Dl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Dl.className="Conv2D";J.registerClass(Dl);var $l=class extends Hu{constructor(t){super(3,t),$l.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};$l.className="Conv3D";J.registerClass($l);var pf=class extends Dl{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=wi(u,m,c,this.padding),h=wi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=mm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=bn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=wi(e[o],u,i,this.padding),e[s]=wi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className="Conv2DTranspose";J.registerClass(pf);var mf=class extends $l{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=wi(l,h,m,this.padding),w=wi(c,g,f,this.padding),I=wi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Ox(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=bn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=wi(e[o],c,a,this.padding),e[s]=wi(e[s],p,u,this.padding),e[i]=wi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};mf.className="Conv3DTranspose";J.registerClass(mf);var Tb=class extends Hu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let n=t[e],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let a=0;a<this.rank;++a)s.push(1);s.push(n*this.depthMultiplier,this.filters);let i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new Ie({ndim:this.rank+2,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{t=St(t);let n;if(this.rank===1)throw new kt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=Cm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=bn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.pointwiseInitializer=_e(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};Tb.className="SeparableConv";var ff=class extends Tb{constructor(t){super(2,t)}};ff.className="SeparableConv2D";J.registerClass(ff);var qu=class extends Hu{constructor(t){super(1,t),qu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};qu.className="Conv1D";J.registerClass(qu);var df=class extends _t{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Ah(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ah(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ah(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ah(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};df.className="Cropping2D";J.registerClass(df);var hf=class extends _t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,j$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?hn.resizeNearestNeighbor(n,[s,i]):hn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?hn.resizeNearestNeighbor(n,[s,i]):hn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};hf.className="UpSampling2D";J.registerClass(hf);function oJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=yn()),Oe(o);let i=Bh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ua(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var gf=class extends Jc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=he(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=oJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=bn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=An(e,this.kernelSize[0],this.padding,this.strides[0]),i=An(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};gf.className="DepthwiseConv2D";J.registerClass(gf);function ZN(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function JN(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(xn(2,u));if(t=Vt(t,l),s!=null)throw new kt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Q(Q(o,"bool"),"float32"),o.rank===u-1&&(o=ar(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;x<f;++x){let b=d[x],w=B(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var Dn=class extends _t{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new ep({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return xn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;n<t;++n)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){if(this.numConstants!=null)throw new kt("Constants support is not implemented in RNN yet.");Hy(t)&&(t=t[0]),t=t;let n=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new Ie({shape:[n,null,...o]});let s=[t[0]].concat(t.slice(2));this.cell.build(s);let i;if(Array.isArray(this.cell.stateSize)?i=this.cell.stateSize:i=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(a=>a.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],i=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,a=[n,i];if(!y.arraysEqual(s.shape,a))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>$e(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=ZN(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=JN((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=pt(e,[1,2]),e=_l(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Gy(e,[1,n]):e):this.cell.stateSize>1?[Gy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Dn.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=Cn(o,n);return new t(Object.assign(e,{cell:s}))}};Dn.className="RNN";J.registerClass(Dn);var Rl=class extends _t{},Qc=class extends Rl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=bi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ol({ones:()=>Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ol({ones:()=>Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Fo($(t,i),this.kernel.read()):s=Fo(t,this.kernel.read()),this.bias!=null&&(s=bn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,Fo(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:yi(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Qc.className="SimpleRNNCell";J.registerClass(Qc);var xf=class extends Dn{constructor(t){t.cell=new Qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};xf.className="SimpleRNN";J.registerClass(xf);var tp=class extends Rl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Qe(this.units,"units"),this.activation=bi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=bi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ol({ones:()=>Ir(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ol({ones:()=>Ir(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0<this.dropout&&this.dropout<1&&(t=$(t,s[0]));let c=Fo(t,this.kernel.read());this.useBias&&(c=bn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,i[0]));let p=this.recurrentKernel.read(),[m,f]=gr(p,[2*this.units,this.units],p.rank-1),d=Fo(o,m),[h,g,x]=gr(c,3,c.rank-1),[b,w]=gr(d,2,d.rank-1);a=this.recurrentActivation.apply(Y(h,b)),u=this.recurrentActivation.apply(Y(g,w));let I=Fo($(u,o),f);l=this.activation.apply(Y(x,I));let N=Y($(a,o),$(Y(1,Ut(a)),l));return[N,N]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:yi(this.activation),recurrentActivation:yi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},t),e)}};tp.className="GRUCell";J.registerClass(tp);var yf=class extends Dn{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new tp(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className="GRU";J.registerClass(yf);var Fl=class extends Rl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=bi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=bi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends wn{apply(u,l){let c=s.apply([i]),p=new Vu().apply([i]),m=s.apply([i*2]);return MN(MN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ol({ones:()=>Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ol({ones:()=>Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0<this.dropout&&this.dropout<1&&(t=$(t,i[0]));let m=Fo(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,a[0])),m=Y(m,Fo(o,this.recurrentKernel.read())),this.useBias&&(m=bn(m,this.bias.read()));let[f,d,h,g]=gr(m,4,m.rank-1);u=this.recurrentActivation.apply(f),l=this.recurrentActivation.apply(d),c=Y($(l,s),$(u,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=$(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:yi(this.activation),recurrentActivation:yi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},t),e)}};Fl.className="LSTMCell";J.registerClass(Fl);var bf=class extends Dn{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Fl(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};bf.className="LSTM";J.registerClass(bf);var ep=class extends Rl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a<this.cells.length;++a){let u=this.cells[a];n=o[a],a===0?i=[t[0]].concat(n):i=[i[0]].concat(n),i=u.call(i,e),s.push(i.slice(1))}n=[];for(let a of s.slice().reverse())n.push(...a);return[i[0]].concat(n)})}build(t){Hy(t)&&(t=t[0]),t=t;let e;this.cells.forEach((n,o)=>{hi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(Cn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return $h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;i<n.weights.length;++i)e.push([n.weights[i],s[i]])}Km(e)}};ep.className="StackedRNNCells";J.registerClass(ep);function Ol(r){let{ones:t,rate:e,training:n=!1,count:o=1,dropoutFunc:s}=r,i=()=>s!=null?s(t(),e):Uy(t(),e),a=()=>Bu(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var sJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)t.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(e[n[o]]=r[n[o]]);return e};var _b=class extends Dn{constructor(t){if(t.unroll)throw new kt("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new kt("It is not possible at the moment to stack convolutional cells.");super(t),this.inputSpec=[new Ie({ndim:5})]}call(t,e){return B(()=>{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a<this.states_.length;++a){let u=t[a],l=s;if(!y.arraysEqual(u.shape,l))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${u.shape}`);this.states_[a]=u}}this.states_=this.states_.map(a=>$e(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=An(l,o[0],s,i[0],a[0]),m=An(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className="ConvRNN2D";var rp=class extends Fl{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,"filters"),this.kernelSize=Uu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=Uu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",gn(this.padding),this.dataFormat=i||"channelsLast",Oe(this.dataFormat),this.dilationRate=Uu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Qe(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends wn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Pm([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ol({ones:()=>Ir(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,at)=>!st||!st[at]?nt:$(st[at],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ol({ones:()=>Ir(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=gr(this.kernel.read(),a,w),[D,F,P,V]=this.useBias?gr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=gr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y($(X,i),$(K,this.activation.apply(Y(m,x)))),et=$(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=sJ(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Tn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?bn(s,n,this.dataFormat):s}recurrentConv(t,e){return Tn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};rp.className="ConvLSTM2DCell";J.registerClass(rp);var wf=class extends _b{constructor(t){let e=new rp(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};wf.className="ConvLSTM2D";J.registerClass(wf);var np=class extends _t{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return n}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(n);return Bu(()=>Uy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};np.className="Dropout";J.registerClass(np);var If=class extends np{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};If.className="SpatialDropout1D";J.registerClass(If);var Cf=class extends _t{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,"units"),this.activation=bi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Py(this.activation.getClassName()),s;return o!=null?s=Fo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Fo(n,this.kernel.read()),this.bias!=null&&(s=bn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:yi(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Dense";J.registerClass(Cf);var vf=class extends _t{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ro(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=Vt(n,o)}return Q$(n)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}};vf.className="Flatten";J.registerClass(vf);var Sf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.activation=bi(t.activation)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:yi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Activation";J.registerClass(Sf);var Nf=class extends _t{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),Z$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Nf.className="RepeatVector";J.registerClass(Nf);var kf=class extends _t{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let n="Total size of new array must be unchanged.",o=e.slice(),s=1,i=null;for(let u=0;u<o.length;++u){let l=o[u];if(this.isUnknown(l))if(i===null)i=u;else throw new z("Can only specifiy one unknown dimension.");else s*=l}let a=Ro(t);if(i!==null){if(s===0||a%s!==0)throw new z(n);o[i]=a/s}else if(a!==s)throw new z(n);return o}computeOutputShape(t){let e=!1;for(let n=0;n<t.length;++n)if(this.isUnknown(t[n])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};kf.className="Reshape";J.registerClass(kf);var Tf=class extends _t{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=xn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Tf.className="Permute";J.registerClass(Tf);var _f=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t),o=-1;return bc(mi(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=bc(mi(n,this.maskValue),o,s);return $(n,Q(i,n.dtype))})}};_f.className="Masking";J.registerClass(_f);var Ef=class extends _t{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(we(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,"inputDim"),this.outputDim=t.outputDim,Qe(this.outputDim,"outputDim"),this.embeddingsInitializer=he(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ve(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),mi(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=we(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o<e.length;++o){let s=e[o],i=t[o+1];if(s!=null&&i!=null&&s!==i)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[n]=i),n++}}return[t[0],...e,this.outputDim]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=rn(n,"int32"));let o=Wy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_e(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Ef.className="Embedding";J.registerClass(Ef);var Pl=class extends _t{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new kt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let n=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],i=e[o];if(s==null||i==null||s<0||i<0)n.push(null);else if(s===1)n.push(i);else if(i===1)n.push(s);else{if(s!==i)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(s)}}return n}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Gt(t)]),t=t,t.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let s of t)s!=null&&s[0]!==null&&e.push(s[0]);if(e=$o(e),e.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;s<t.length;++s){let i=t[s]==null?null:t[s].slice(1);n=this.computeElementwiseOpOutputShape(n,i)}let o=t.map(s=>s.length);t.indexOf(null)===-1&&$o(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=gi(o);for(let i of t){let a=i.rank;for(let u=0;u<s-a;++u)i=_l(i,1);n.push(i)}return this.mergeFunction(n)}else{let s=!1;for(let u of t){let l=u.rank;if(l==null){let c=u.shape,p=c[0],m=c.slice(1).concat([p]),f=R(u,[p].concat(Ro(c.slice(1))));f=Vt(f,[1,0]),f=R(f,m),n.push(f),s=!0}else if(l>1){let c=xn(1,l).concat([0]);n.push(Vt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Vt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(xn(0,a-1));i=Vt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o<t.length;++o){let s=t[o]==null?null:t[o].slice(1);e=this.computeElementwiseOpOutputShape(e,s)}let n=[];for(let o of t)o!=null&&o[0]!==null&&n.push(o[0]);return n=$o(n),n.length===1?e=n.concat(e):e=[null].concat(e),e}computeMask(t,e){return B(()=>{if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an Array");if(!Array.isArray(t))throw new z("`inputs` should be an Array");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ar(o,0));let n=e[0];for(let o=1;o<e.length-1;++o)n=Pr(n,e[o]);return n})}},Af=class extends Pl{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0].clone();for(let n=1;n<t.length;++n)e=Y(e,t[n]);return e})}};Af.className="Add";J.registerClass(Af);var Df=class extends Pl{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0].clone();for(let n=1;n<t.length;++n)e=$(e,t[n]);return e})}};Df.className="Multiply";J.registerClass(Df);var $f=class extends Pl{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0].clone();for(let n=1;n<t.length;++n)e=Y(e,t[n]);return $(1/t.length,e)})}};$f.className="Average";J.registerClass($f);var Rf=class extends Pl{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0];for(let n=1;n<t.length;++n)e=_n(e,t[n]);return e})}};Rf.className="Maximum";J.registerClass(Rf);var Ff=class extends Pl{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0];for(let n=1;n<t.length;++n)e=mo(e,t[n]);return e})}};Ff.className="Minimum";J.registerClass(Ff);var Of=class extends Pl{constructor(t){super(t),this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let o of t)if(o!=null){e=!1;break}if(e)return;let n=[];for(let o=0;o<t.length;++o){let s=t[o].slice();s.splice(this.axis,1);let i=!1;for(let a of n)if(y.arraysEqual(a,s)){i=!0;break}i||n.push(s)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return B(()=>Pm(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let e=t,n=e[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of e.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new z("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new z(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return B(()=>{let n=!0;if(e.forEach(i=>{if(i!=null){n=!1;return}}),n)return null;let o=[];for(let i=0;i<t.length;++i)e[i]==null?o.push(Q(Ir(t[i]),"bool")):e[i].rank<t[i].rank?o.push(ar(e[i],-1)):o.push(e[i]);let s=ie(o,this.axis);return lm(s,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};Of.className="Concatenate";J.registerClass(Of);function Vh(r,t){for(;r<0;)r+=t;return r}function iJ(r,t,e){if(r.shape.length>3||t.shape.length>3)throw new kt("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new kt("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;l<i;++l)u.push(1);t=R(t,t.shape.concat(u))}else if(o>n){i=o-n;let u=[];for(let l=0;l<i;++l)u.push(1);r=R(r,r.shape.concat(u))}else i=0;let a;if(r.shape.length===2&&t.shape.length===2)s[0]===s[1]?a=pt($(r,t),s[0]):a=pt($(Vt(r,[1,0]),t),s[1]);else{let u=s[0]!==r.shape.length-1,l=s[1]===t.shape.length-1;a=Bt(r,t,u,l)}if(i>0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);a=qn(a,l)}return a.shape.length===1&&(a=ar(a,1)),a})}var Pf=class extends Pl{constructor(t){super(t),this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new kt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Vh(s,t[i].shape.length)):o=[Vh(this.axes,e.shape.length),Vh(this.axes,n.shape.length)],this.normalize&&(e=Rh(e,o[0]),n=Rh(n,o[1])),iJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Vh(this.axes,t.length),Vh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new kt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Pf.className="Dot";J.registerClass(Pf);var Mf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Bu(()=>Y(Mm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Mf.className="GaussianNoise";J.registerClass(Mf);var Lf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Bu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Mm(n.shape,1,s))},()=>n,e.training||!1):n})}};Lf.className="GaussianDropout";J.registerClass(Lf);var zf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Bu(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=mn(Hn(n),this.rate);l=rn(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=Y($(s,l),$(Y(l,-1),u));return Y($(m,c),p)},()=>St(t),e.training||!1)}return t})}};zf.className="AlphaDropout";J.registerClass(zf);function Gh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Sx(r,t,e,n,o,s);else if(r.rank===3)i=Nx(r,t,e,n,o,s);else if(r.rank===4)i=kx(r,t,e,n,o,s);else throw new kt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function aJ(r,t,e,n,o=.001){return B(()=>{let s=vc(r,n),i=s.mean,a=s.variance;return[Gh(r,i,a,e,t,o),i,a]})}function lJ(r,t,e,n,o=.001){return B(()=>{let s=vc(r,n),i=s.mean,a=s.variance,u=[];for(let d of xn(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[Gh(r,l,c,m,p,o),i,a]})}function uJ(r,t,e,n,o=.001){return y.arraysEqual(n.slice().sort(),xn(0,r.rank-1))?aJ(r,t,e,n,o):lJ(r,t,e,n,o)}var Bf=class extends _t{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=he(t.betaInitializer||"zeros"),this.gammaInitializer=he(t.gammaInitializer||"ones"),this.movingMeanInitializer=he(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=he(t.movingVarianceInitializer||"ones"),this.betaConstraint=Ve(t.betaConstraint),this.gammaConstraint=Ve(t.gammaConstraint),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer)}build(t){t=Gt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=xn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=Ao(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,xn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Gh(o,b,w,I,N,this.epsilon)}else return Gh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=uJ(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(lt(E,w),N);b.write(lt(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),movingMeanInitializer:_e(this.movingMeanInitializer),movingVarianceInitializer:_e(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Bf.className="BatchNormalization";J.registerClass(Bf);var Vf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=he(t.betaInitializer||"zeros"),this.gammaInitializer=he(t.gammaInitializer||"ones"),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==$o(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=vc(n,this.axis,!0),l=Ao(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return a=Or(a,f),u=Or(u,f),p!=null&&(p=Or(p,d)),m!=null&&(m=Or(m,d)),Gh(n,a,u,m,p,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}};Vf.className="LayerNormalization";J.registerClass(Vf);function cJ(r,t,e){return B(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=yn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],dn(r,n)})}var Gf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?yn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>cJ(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Gf.className="ZeroPadding2D";J.registerClass(Gf);function Fb(r,t,e,n,o,s){return B(()=>{Oe(o),RN(s),gn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=yn()),s==null&&(s="max"),r=Bh(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Du(r,t,e,a):i=Su(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function BR(r,t,e,n,o,s){return B(()=>{Oe(o),RN(s),gn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=yn()),s==null&&(s="max"),r=YN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Jx(r,t,e,a):i=vx(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Eb=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Qe(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,gn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=An(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=_l(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return qn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Wf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"max")}};Wf.className="MaxPooling1D";J.registerClass(Wf);var Uf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"avg")}};Uf.className="AveragePooling1D";J.registerClass(Uf);var Ab=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),gn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Hf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"max")}};Hf.className="MaxPooling2D";J.registerClass(Hf);var qf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"avg")}};qf.className="AveragePooling2D";J.registerClass(qf);var Db=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),gn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),o=An(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Kf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),BR(t,e,n,o,s,"max")}};Kf.className="MaxPooling3D";J.registerClass(Kf);var jf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),BR(t,e,n,o,s,"avg")}};jf.className="AveragePooling3D";J.registerClass(jf);var $b=class extends _t{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new kt}},Xf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return ke(n,1)})}};Xf.className="GlobalAveragePooling1D";J.registerClass(Xf);var Yf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Nr(n,1)})}};Yf.className="GlobalMaxPooling1D";J.registerClass(Yf);var Rb=class extends _t{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new kt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Zf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?ke(n,[1,2]):ke(n,[2,3])})}};Zf.className="GlobalAveragePooling2D";J.registerClass(Zf);var Jf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Nr(n,[1,2]):Nr(n,[2,3])})}};Jf.className="GlobalMaxPooling2D";J.registerClass(Jf);var Ob=class extends _t{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=Cn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Qf=class extends Ob{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),JN((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Qf.className="TimeDistributed";J.registerClass(Qf);function pJ(r){ya(q$,"BidirectionalMergeMode",r)}var mJ="concat",td=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=Cn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=Cn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?mJ:t.mergeMode,pJ(this.mergeMode),t.weights)throw new kt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=ZN(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new Ie({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new kt("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=hr(s,1));let a;return this.mergeMode==="concat"?a=Pm([o,s]):this.mergeMode==="sum"?a=Y(o,s):this.mergeMode==="ave"?a=$(.5,Y(o,s)):this.mergeMode==="mul"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){hi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),hi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=Cn(e.layer);if(delete e.layer,e.numConstants!=null)throw new kt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};td.className="Bidirectional";J.registerClass(td);var ed=class extends _t{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!=="float32"&&(t=rn(t,"float32")),Y($(t,this.scale),this.offset)))}};ed.className="Rescaling";J.registerClass(ed);var{resizeBilinear:fJ,cropAndResize:dJ}=hn,rd=class extends _t{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N<l.shape[0];N++)g.push(h);let x=sr(g,[g.length,4]),b=da(0,g.length,1,"int32"),I=dJ(l,x,b,[o,s],"nearest");return c?rn(St(xr(I)),u):rn(I,u)})}upsize(t,e,n,o){return B(()=>{let s=fJ(t,[e,n]);return rn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};rd.className="CenterCrop";J.registerClass(rd);function VR(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=rn(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=ar(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=ar(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=gh(a,n,e,i):u=gh(a,[],e,i),t!=="tfIdf")return u;if(n)return $(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var nd=class extends _t{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=rn(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count.
              Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Nr(t),s=bl(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=mn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return VR(t,this.outputMode,this.numTokens,n)})}};nd.className="CategoryEncoding";J.registerClass(nd);var gJ=["bilinear","nearest"],GR=new Set(gJ),od=class extends _t{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(GR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return hn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return hn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...GR]} are supported`)})}};od.className="Resizing";J.registerClass(od);var Wh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Wh.className="RandomSeed";var Uh=class extends _t{constructor(t){super(t),this.randomGenerator=new Wh(t.seed)}getConfig(){let t={seed:this.randomGenerator.seed},e=super.getConfig();return Object.assign(t,e),t}};Uh.className="BaseRandomLayer";var xJ=["bilinear","nearest"],WR=new Set(xJ),sd=class extends Uh{constructor(t){super(t);let{factor:e,interpolation:n="bilinear"}=t;if(this.factor=e,Array.isArray(this.factor)&&this.factor.length===2)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else if(!Array.isArray(this.factor)&&this.factor>0)this.widthLower=-this.factor,this.widthUpper=this.factor;else throw new z(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);if(this.widthLower<-1||this.widthUpper<-1)throw new z(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper<this.widthLower)throw new z(`factor cannot have upper bound less than lower bound.
        Got upper bound: ${this.widthUpper}.
        Got lower bound: ${this.widthLower}
      `);if(n)if(WR.has(n))this.interpolation=n;else throw new z(`Invalid interpolation parameter: ${n} is not implemented`)}getConfig(){let t={factor:this.factor,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.imgHeight,-1,e]}call(t,e){return B(()=>{let n=St(t);this.imgHeight=n.shape[n.shape.length-3];let o=n.shape[n.shape.length-2];this.widthFactor=Hn([1],1+this.widthLower,1+this.widthUpper,"float32",this.randomGenerator.next());let s=this.widthFactor.dataSync()[0]*o;s=Math.round(s);let i=[this.imgHeight,s];switch(this.interpolation){case"bilinear":return hn.resizeBilinear(t,i);case"nearest":return hn.resizeNearestNeighbor(t,i);default:throw new Error(`Interpolation is ${this.interpolation}
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n!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=Jb(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let p=0;p<t.length;++p){let f=[0,p===0?0:o[p-1],0],d=[1,t[p],a];c[p]=R(Pt(r,f,d),i)}return r.dispose(),c}),l=new Ml([],e,r.dtype,t.length);for(let c=0;c<u.length;c++)l.setItem(c,u[c]);return l}var cF=async(r,t,e)=>{switch(r.op){case"If":case"StatelessIf":{let n=v("thenBranch",r,t,e),o=v("elseBranch",r,t,e),s=v("cond",r,t,e),i=v("args",r,t,e);return(await s.data())[0]?e.functionMap[n].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap):e.functionMap[o].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let n=v("body",r,t,e),o=v("cond",r,t,e),s=v("args",r,t,e),i=await e.functionMap[o].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),a=s.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&a.indexOf(c.id)===-1&&c.dispose()});let l=s;for(;u[0];){let c=l;l=await e.functionMap[n].executeFunctionAsync(l,e.tensorArrayMap,e.tensorListMap);let p=l.map(f=>f.id);c.forEach(f=>{!f.kept&&a.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()});let m=await e.functionMap[o].executeFunctionAsync(l,e.tensorArrayMap,e.tensorListMap);u=await m[0].data(),m.forEach(f=>{!f.kept&&a.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()})}return l}case"LoopCond":{let n=v("pred",r,t,e);return[Ci(n)]}case"Switch":{let n=v("pred",r,t,e),o=v("data",r,t,e);return o.kept||(o=Ci(o)),(await n.data())[0]?[void 0,o]:[o,void 0]}case"Merge":{let n=r.inputNames.find(o=>pr(o,t,e)!==void 0);if(n){let o=pr(n,t,e);return[Ci(o)]}return}case"Enter":{let n=v("frameName",r,t,e),o=v("tensor",r,t,e);return e.enterFrame(n),[Ci(o)]}case"Exit":{let n=v("tensor",r,t,e);return e.exitFrame(),[Ci(n)]}case"NextIteration":{let n=v("tensor",r,t,e);return e.nextIteration(),[Ci(n)]}case"TensorArrayV3":{let n=v("size",r,t,e),o=v("dtype",r,t,e),s=v("elementShape",r,t,e),i=v("dynamicSize",r,t,e),a=v("clearAfterRead",r,t,e),u=v("identicalElementShapes",r,t,e),l=v("name",r,t,e),c=new Qb(l,o,n,s,u,i,a);return e.addTensorArray(c),[c.idTensor,ft(1)]}case"TensorArrayWriteV3":{let n=v("tensorArrayId",r,t,e),o=v("index",r,t,e),s=v("tensor",r,t,e),i=e.getTensorArray(n.id);return i.write(o,s),[i.idTensor]}case"TensorArrayReadV3":{let n=v("tensorArrayId",r,t,e),o=v("index",r,t,e);return[e.getTensorArray(n.id).read(o)]}case"TensorArrayGatherV3":{let n=v("tensorArrayId",r,t,e),o=v("indices",r,t,e),s=v("dtype",r,t,e);return[e.getTensorArray(n.id).gather(o,s)]}case"TensorArrayScatterV3":{let n=v("tensorArrayId",r,t,e),o=v("indices",r,t,e),s=v("tensor",r,t,e),i=e.getTensorArray(n.id);return i.scatter(o,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=v("tensorArrayId",r,t,e),o=e.getTensorArray(n.id),s=v("dtype",r,t,e);return[o.concat(s)]}case"TensorArraySplitV3":{let n=v("tensorArrayId",r,t,e),o=v("tensor",r,t,e),s=v("lengths",r,t,e),i=e.getTensorArray(n.id);return i.split(s,o),[i.idTensor]}case"TensorArraySizeV3":{let n=v("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return[ft(o.size(),"int32")]}case"TensorArrayCloseV3":{let n=v("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return o.clearAndClose(),[o.idTensor]}case"TensorListSetItem":{let n=v("tensorListId",r,t,e),o=v("index",r,t,e),s=v("tensor",r,t,e),i=e.getTensorList(n.id);return i.setItem(o,s),[i.idTensor]}case"TensorListGetItem":{let n=v("tensorListId",r,t,e),o=v("index",r,t,e),s=v("elementShape",r,t,e),i=v("elementDType",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=v("indices",r,t,e),o=v("tensor",r,t,e),s=v("elementShape",r,t,e),i=v("numElements",r,t,e),a=lF(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=v("elementShape",r,t,e),o=v("elementDType",r,t,e),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=v(s,r,t,e),a=r.op==="TensorListReserve"?-1:i,u=aF(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case"TensorListGather":{let n=v("tensorListId",r,t,e),o=v("indices",r,t,e),s=v("elementShape",r,t,e),i=v("elementDType",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case"TensorListStack":{let n=v("tensorListId",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e),i=v("numElements",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case"TensorListFromTensor":{let n=v("tensor",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e),i=iF(n,o,s);return e.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=v("tensorListId",r,t,e),o=e.getTensorList(n.id),s=v("dtype",r,t,e),i=v("elementShape",r,t,e);return[o.concat(s,i)]}case"TensorListPushBack":{let n=v("tensorListId",r,t,e),o=v("tensor",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=v("tensorListId",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=v("tensor",r,t,e),o=v("elementShape",r,t,e),s=v("lengths",r,t,e),i=uF(n,s,o);return e.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=v("tensorListId",r,t,e),o=e.getTensorList(n.id);return[ft(o.size(),"int32")]}case"TensorListResize":{let n=v("tensorListId",r,t,e),o=v("size",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function pF(r,t,e){let[n,o]=v("fusedOps",r,t,e),s=n==="biasadd",i=!s,a=o==="prelu",u=n==="fusedbatchnorm",l=v("numArgs",r,t,e);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&s&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=v("strides",r,t,e),p=Hh(r,t,e),m=v("dataFormat",r,t,e).toUpperCase(),f=v("dilations",r,t,e),[d,h]=v("args",r,t,e);i&&(h=d,d=void 0);let g=v("leakyreluAlpha",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var mF=(r,t,e,n=ae)=>{switch(r.op){case"Conv1D":{let o=v("stride",r,t,e),s=v("pad",r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilation",r,t,e);return[n.conv1d(v("x",r,t,e),v("filter",r,t,e),o,s,i,a)]}case"Conv2D":{let o=v("strides",r,t,e),s=Hh(r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilations",r,t,e);return[n.conv2d(v("x",r,t,e),v("filter",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=pF(r,t,e);return[n.fused.conv2d({x:v("x",r,t,e),filter:v("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=pF(r,t,e);return[n.fused.depthwiseConv2d({x:v("x",r,t,e),filter:v("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let o=v("outputShape",r,t,e),s=v("strides",r,t,e),i=Hh(r,t,e);return[n.conv2dTranspose(v("x",r,t,e),v("filter",r,t,e),o,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let o=v("strides",r,t,e),s=Hh(r,t,e),i=v("dilations",r,t,e),a=v("dataFormat",r,t,e).toUpperCase();return[n.depthwiseConv2d(v("input",r,t,e),v("filter",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case"Conv3D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilations",r,t,e);return[n.conv3d(v("x",r,t,e),v("filter",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case"AvgPool":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.avgPool(v("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPool":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.maxPool(v("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPoolWithArgmax":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e),a=v("includeBatchInIndex",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(v("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case"AvgPool3D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.avgPool3d(v("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"MaxPool3D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.maxPool3d(v("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"Dilation2D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("dilations",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(v("x",r,t,e),v("filter",r,t,e),[a,u],s,[l,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var fF=(r,t,e,n=ae)=>{switch(r.op){case"Fill":{let o=v("shape",r,t,e),s=v("dtype",r,t,e),i=v("value",r,t,e);return[n.fill(o,i,s)]}case"LinSpace":{let o=v("start",r,t,e),s=v("stop",r,t,e),i=v("num",r,t,e);return[n.linspace(o,s,i)]}case"Multinomial":{let o=v("logits",r,t,e),s=v("numSamples",r,t,e),i=v("seed",r,t,e);return[n.multinomial(o,s,i)]}case"OneHot":{let o=v("indices",r,t,e),s=v("depth",r,t,e),i=v("onValue",r,t,e),a=v("offValue",r,t,e),u=v("dtype",r,t,e);return[n.oneHot(o,s,i,a,u)]}case"Ones":return[n.ones(v("shape",r,t,e),v("dtype",r,t,e))];case"OnesLike":return[n.onesLike(v("x",r,t,e))];case"RandomStandardNormal":return[n.randomStandardNormal(v("shape",r,t,e),v("dtype",r,t,e),v("seed",r,t,e))];case"RandomUniform":return[n.randomUniform(v("shape",r,t,e),v("minval",r,t,e),v("maxval",r,t,e),v("dtype",r,t,e))];case"RandomUniformInt":return[n.randomUniformInt(v("shape",r,t,e),v("minval",r,t,e),v("maxval",r,t,e),v("seed",r,t,e))];case"Range":{let o=v("start",r,t,e),s=v("stop",r,t,e),i=v("step",r,t,e);return[n.range(o,s,i,v("dtype",r,t,e))]}case"TruncatedNormal":{let o=v("shape",r,t,e),s=v("mean",r,t,e),i=v("stdDev",r,t,e),a=v("seed",r,t,e);return[n.truncatedNormal(o,s,i,v("dtype",r,t,e),a)]}case"Zeros":return[n.zeros(v("shape",r,t,e),v("dtype",r,t,e))];case"ZerosLike":return[n.zerosLike(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Sk(r,t,e){let n=v("boxes",r,t,e),o=v("scores",r,t,e),s=v("maxOutputSize",r,t,e),i=v("iouThreshold",r,t,e),a=v("scoreThreshold",r,t,e),u=v("softNmsSigma",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var dF=async(r,t,e,n,o=ae)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=Sk(r,t,e),p=await o.image.nonMaxSuppressionWithScoreAsync(s,i,a,u,l,c);return[p.selectedIndices,p.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=Sk(r,t,e),c=v("padToMaxOutputSize",r,t,e),p=await o.image.nonMaxSuppressionPaddedAsync(s,i,a,u,l,c);return[p.selectedIndices,p.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=Sk(r,t,e);return[await o.image.nonMaxSuppressionAsync(s,i,a,u,l)]}case"Where":{let s=o.cast(v("condition",r,t,e),"bool"),i=[await o.whereAsync(s)];return s.dispose(),i}case"ListDiff":return o.setdiff1dAsync(v("x",r,t,e),v("y",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var hF=(r,t,e,n=ae)=>{switch(r.op){case"LowerBound":{let o=v("sortedSequence",r,t,e),s=v("values",r,t,e);return[n.lowerBound(o,s)]}case"TopKV2":{let o=v("x",r,t,e),s=v("k",r,t,e),i=v("sorted",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case"UpperBound":{let o=v("sortedSequence",r,t,e),s=v("values",r,t,e);return[n.upperBound(o,s)]}case"Unique":{let o=v("x",r,t,e),s=n.unique(o);return[s.values,s.indices]}case"UniqueV2":{let o=v("x",r,t,e),s=v("axis",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gF=(r,t,e,n=ae)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let o=v("default",r,t,e);return[pr(r.name,t,e)||o];case"Placeholder":return[pr(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=v("x",r,t,e);return[Ci(c)]}case"IdentityN":return v("x",r,t,e).map(c=>Ci(c));case"Snapshot":let s=v("x",r,t,e);return[Ci(s)];case"Shape":return[n.tensor1d(v("x",r,t,e).shape,"int32")];case"ShapeN":return v("x",r,t,e).map(c=>n.tensor1d(c.shape));case"Size":return[n.scalar(v("x",r,t,e).size,"int32")];case"Rank":return[n.scalar(v("x",r,t,e).rank,"int32")];case"NoOp":return[n.scalar(1)];case"Print":let i=v("x",r,t,e),a=v("data",r,t,e),u=v("message",r,t,e),l=v("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(u);for(let c=0;c<a.length;c++)console.log(Array.prototype.slice.call(a[c].dataSync()).slice(0,l));return[i];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tw=class{get id(){return this.handle.id}constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=ft(0),this.tensorMap=new Map,$e(this.handle)}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ft(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=xr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a<s;a++){let u=n[a],l=o[a];$e(l),this.tensorMap.set(u,l)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return B(()=>{let o=[];for(let s=0;s<n.length;s++){let i=n[s],a=this.findWithDefault(i,e);o.push(a)}return qe(o)})}findWithDefault(t,e){let n=this.tensorMap.get(t);return n!=null?n:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}};var xF=async(r,t,e,n)=>{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v("keyDType",r,t,e),i=v("valueDType",r,t,e),a=new tw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("defaultValue",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let o=v("tableHandle",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var yF=(r,t,e,n=ae)=>{switch(r.op){case"ResizeBilinear":{let o=v("images",r,t,e),s=v("size",r,t,e),i=v("alignCorners",r,t,e),a=v("halfPixelCenters",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case"ResizeNearestNeighbor":{let o=v("images",r,t,e),s=v("size",r,t,e),i=v("alignCorners",r,t,e),a=v("halfPixelCenters",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case"CropAndResize":{let o=v("image",r,t,e),s=v("boxes",r,t,e),i=v("boxInd",r,t,e),a=v("cropSize",r,t,e),u=v("method",r,t,e),l=v("extrapolationValue",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case"ImageProjectiveTransformV3":{let o=v("images",r,t,e),s=v("transforms",r,t,e),i=v("outputShape",r,t,e),a=v("fillValue",r,t,e),u=v("interpolation",r,t,e),l=v("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var bF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var wF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var IF=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var CF=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let a=v("axis",r,t,e);return[n.argMax(v("x",r,t,e),a)]}case"ArgMin":{let a=v("axis",r,t,e);return[n.argMin(v("x",r,t,e),a)]}case"Prod":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.prod(v("x",r,t,e),a,u)]}case"Cumprod":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumprod(v("x",r,t,e),a,u,l)]}case"Cumsum":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumsum(v("x",r,t,e),a,u,l)]}case"Bincount":let o=v("x",r,t,e),s=v("weights",r,t,e),i=v("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=v("x",r,t,e),u=v("weights",r,t,e),l=v("size",r,t,e),c=v("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var SF=(r,t,e,n=ae)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=v("n",r,t,e),s=v("axis",r,t,e),i=v("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=v("axis",r,t,e),s=v("batchDims",r,t,e),i=v("x",r,t,e),a=v("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=v("dims",r,t,e),s=[];for(let a=0;a<o.length;a++)o[a]&&s.push(a);let i=v("x",r,t,e);return[n.reverse(i,s)]}case"ReverseV2":{let o=v("axis",r,t,e),s=v("x",r,t,e);return[n.reverse(s,o)]}case"Slice":{let o=v("begin",r,t,e),s=v("size",r,t,e);return[n.slice(v("x",r,t,e),o,s)]}case"StridedSlice":{let o=v("begin",r,t,e),s=v("end",r,t,e),i=v("strides",r,t,e),a=v("beginMask",r,t,e),u=v("endMask",r,t,e),l=v("ellipsisMask",r,t,e),c=v("newAxisMask",r,t,e),p=v("shrinkAxisMask",r,t,e),m=v("x",r,t,e);return[n.stridedSlice(m,o,s,i,a,u,l,c,p)]}case"Pack":return B(()=>{let o=v("axis",r,t,e),s=v("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=v("axis",r,t,e),s=v("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=v("reps",r,t,e);return[n.tile(v("x",r,t,e),o)]}case"Split":case"SplitV":{let o=v("axis",r,t,e),s=v("numOrSizeSplits",r,t,e),i=v("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var NF=(r,t,e,n=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var TF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let o=v("axis",r,t,e);return[n.squeeze(v("x",r,t,e),o)]}case"Reshape":return[n.reshape(v("x",r,t,e),v("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(v("x",r,t,e),v("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(v("x",r,t,e),v("padding",r,t,e),v("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(v("x",r,t,e),v("padding",r,t,e),v("constantValue",r,t,e))];case"SpaceToBatchND":{let o=v("blockShape",r,t,e),s=v("paddings",r,t,e);return[n.spaceToBatchND(v("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=v("blockShape",r,t,e),s=v("crops",r,t,e);return[n.batchToSpaceND(v("x",r,t,e),o,s)]}case"DepthToSpace":{let o=v("blockSize",r,t,e),s=v("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(v("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(v("x",r,t,e),v("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(v("s0",r,t,e),v("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Nk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>nF(i,a,u));case"basic_math":return o(()=>oF(i,a,u));case"control":return cF(i,a,u);case"convolution":return o(()=>mF(i,a,u));case"creation":return o(()=>fF(i,a,u));case"dynamic":return dF(i,a,u);case"evaluation":return o(()=>hF(i,a,u));case"image":return o(()=>yF(i,a,u));case"graph":return o(()=>gF(i,a,u));case"logical":return o(()=>bF(i,a,u));case"matrices":return o(()=>wF(i,a,u));case"normalization":return o(()=>IF(i,a,u));case"ragged":return o(()=>CF(i,a,u));case"reduction":return o(()=>vF(i,a,u));case"slice_join":return o(()=>SF(i,a,u));case"sparse":return o(()=>NF(i,a,u));case"spectral":return o(()=>kF(i,a,u));case"string":return o(()=>TF(i,a,u));case"transformation":return o(()=>_F(i,a,u));case"hash_table":return xF(i,a,u,n);case"custom":let l=zb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Zb(i,a,u));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()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Kh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let n=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(n))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function kk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>vn(m)[0]));n=n||[];let c=new Set(n.map(m=>vn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Ku(m)||jQ(m)||XQ(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function EF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>vn(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=WQ(d,u);return UQ(h,u),h}function WQ(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var ad=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function UQ(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new ad(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new ad(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new ad(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new ad(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function AF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Ku(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;a<r.length;++a){let u=s[a];if(u===e)continue;let l=r[a],c=r[u];i.has(c.name)||i.set(c.name,[]),i.get(c.name).push(l)}return i}var HQ=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),qQ=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),KQ=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Ku(r){return HQ.has(r.op)}function jQ(r){return qQ.has(r.op)}function XQ(r){return KQ.has(r.op)}var op=class{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new op(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=kk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=EF(this.graph,n),u=AF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[vn(m)[0]]),s=e.map(m=>vn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Kh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=vn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Nk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Ku(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Ku(u))continue;let l=nk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Ku(u)||s.has(u.name)}if(!(Ku(t)||i==null))for(let u of i){if(a(u))continue;let l=nk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Kh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[vn(I)[0]]),a=n.map(I=>vn(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=kk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=vn(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Ku(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&v("isConstant",p.node,o,n)&&([m]=Ii(p.node.name,n)),o[p.node.name]==null){let f=Nk(p.node,o,n,this._resourceManager);m||([m]=Ii(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Ii(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=vn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=vn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=vn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var YQ="?tfjs-format=file",ZQ="model.json",jh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Lr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new ew}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new op(qh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=qh.Instance.transformGraph(t.modelInitializer);this.initializer=new op(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Ot?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Ot)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o<n.length;o++){let s=n[o],i=e[s];this.resourceIdToCapturedInput[i.resourceId]=t[o]}}}execute(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=this.executor.execute(t,e);return n.length>1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function JQ(r,t={},e=Lr){if(r==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&&typeof r=="string"&&(r=ttt(r));let n=new jh(r,t,e);return await n.load(),n}function QQ(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(r instanceof Array){let[n,o]=r;if(!n)throw new Error("modelJSON must be the first element of the array");if(!o||!(o instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=Lr.getWeightSpecs(n.weightsManifest),i=Lr.getModelArtifactsForJSONSync(n,s,o);t=Lr.fromMemorySync(i)}else if("load"in r)t=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)t=Lr.fromMemorySync(r);else throw new Error("Unknown model format");let e=new jh(t);return e.load(),e}function ttt(r){return r.endsWith("/")||(r=r+"/"),`${r}${ZQ}${YQ}`}var DF="4.7.0";var ZF={};Kt(ZF,{CSVDataset:()=>cd,Dataset:()=>vi,FileDataSource:()=>hd,TextLineDataset:()=>ud,URLDataSource:()=>gd,array:()=>VF,csv:()=>qF,func:()=>KF,generator:()=>jF,microphone:()=>YF,version_data:()=>Kk,webcam:()=>XF,zip:()=>GF});var BF=Xl(bh());var MF=Xl(bh());function $F(r,t){return rw(r,t)}function rw(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob=="function"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error("Circular references are not supported.");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(ju(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=rw(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function RF(r,t=_k){return FF(r,t)}function FF(r,t,e=new Set){let n=r[0];if(e.has(n))throw new Error("Circular references are not supported.");let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(ju(n)){let s=Array.isArray(n)?[]:{};e.add(n);for(let i in n){let a=r.map(l=>l[i]),u=FF(a,t,e);s[i]=u}return e.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return o.value}function _k(r){return r===null?null:ju(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function nw(r,t){let e=new Map;rw(r,t,e);for(let o of Array.from(e.keys())){let s=e.get(o);if(y.isPromise(s)){let i=await s;e.set(o,i)}}return rw(r,t,e)}function ju(r){let t=!1;if(L().get("IS_BROWSER"))t=r instanceof TextDecoder;else{let{StringDecoder:e}=Tk();t=r instanceof e}return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Ot)&&!(r instanceof Promise)&&!t)}function OF(r){return r==null||ett(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Ot||y.isTypedArray(r)}function ett(r){return r===null||typeof r!="object"&&typeof r!="function"}function PF(r){return $F(r,rtt)}function rtt(r){return r instanceof Ot?{value:r.clone(),recurse:!1}:ju(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var ld=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),n=this.get(e);return this.set(e,this.pop()),n}};var sp=class extends ld{constructor(){super(sp.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;o<n;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};sp.INITIAL_CAPACITY=32;function Vk(r){return new Ek(r)}function Xh(r){return new Ak(r)}function LF(r,t){return new sw(r,t)}function zF(r,t=Ll.FAIL){return new zk(r,t)}var tr=class{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],n=await t.next();for(;!n.done;)e.push(n.value),n=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),n=t(e.value);for(;!e.done&&n;)e=await this.next(),n=t(e.value)}handleErrors(t){return new Mk(this,t)}filter(t){return new Ok(this,t)}map(t){return new Pk(this,t)}mapAsync(t){return new ow(this,t)}serialMapAsync(t){return new ow(this,t).serial()}flatmap(t){return new Lk(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new Fk(this,t,e)}columnMajorBatch(t,e=!0,n=_k){return this.rowMajorBatch(t,e).map(s=>RF(s,n))}concatenate(t,e){return new sw(Vk([this,t]),e)}take(t){return t<0||t==null?this:new Rk(this,t)}skip(t){return t<0||t==null?this:new $k(this,t)}prefetch(t){return new iw(this,t)}shuffle(t,e){return new Bk(this,t,e)}serial(){return new Dk(this)}},Ek=class extends tr{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:PF(t),done:!1}}},Ak=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Dk=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},$k=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Tt(t.value)}return this.upstream.next()}},Rk=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Fk=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Ok=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Pk=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=So.getTensorsInContainer(t.value),n=this.transform(t.value),o=So.getTensorsInContainer(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Mk=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},ow=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=So.getTensorsInContainer(t.value),n=await this.transform(t.value),o=So.getTensorsInContainer(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ip=class extends tr{constructor(){super(),this.outputQueue=new sp,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}}},Lk=class extends ip{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=So.getTensorsInContainer(t.value),n=this.transform(t.value),o=So.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return!0}},sw=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Ll;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Ll||(Ll={}));var zk=class extends tr{constructor(t,e=Ll.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof tr?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await nw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ll.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ll.SHORTEST:return{value:null,done:!0};case Ll.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},iw=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new ld(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},Bk=class extends iw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=MF.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var vi=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is
      ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),$n(async()=>(await n.iterator()).columnMajorBatch(t,e,ntt),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,$n(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,$n(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return $n(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return $n(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return $n(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,$n(async()=>{let o=Xh(async()=>({value:await e.iterator(),done:!1}));return LF(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?n=0:n=null,$n(async()=>(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=BF.alea(e||y.now().toString());return $n(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,$n(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};vi.MAX_BUFFER_SIZE=1e4;function $n(r,t=null){return new class extends vi{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function VF(r){return $n(async()=>Vk(r),r.length)}function GF(r){if(!ju(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e<r.length;e++)t=t==null?r[e].size:Math.min(t,r[e].size);else if(r instanceof Object)for(let e in r)t=t==null?r[e].size:Math.min(t,r[e].size);return $n(async()=>{let e=await nw(r,n=>{if(n instanceof vi)return{value:n.iterator(),recurse:!1};if(ju(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return zF(e,Ll.SHORTEST)},t)}function ntt(r){if(r===null)return null;let t=r[0];return OF(t)?{value:ott(r),recurse:!1}:{value:null,recurse:!0}}function ott(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ot?qe(r):sr(r)}var ud=class extends vi{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var aw='"',Yh=Symbol("out"),WF=Symbol("field"),lw=Symbol("quote"),Gk=Symbol("quoteafterquote"),UF=Symbol("quoteinquote"),cd=class extends vi{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new ud(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let i=this.fullColumnNames[s],a=this.columnConfigs?this.columnConfigs[i]:null;if(!(this.configuredColumnsOnly&&!a)){let u=e[s],l=null;if(u==="")if(a&&a.default!==void 0)l=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${i} is empty in this line: ${t}`);l=void 0}else{let c=Number(u);if(isNaN(c))a&&a.dtype==="bool"?l=this.getBoolean(u):l=u;else if(!a||!a.dtype)l=c;else switch(a.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(u);break;default:l=c}}a&&a.isLabel?o[i]=l:n[i]=l}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let n=[],o=0,s=t.length,i=Yh;for(let a=0;a<s;a++)switch(i){case Yh:switch(t.charAt(a)){case aw:o=a+1,i=lw;break;case this.delimiter:if(o=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),i=Yh;break;default:i=WF,o=a;break}break;case WF:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a)),i=Yh,o=a+1;break;default:}break;case lw:switch(t.charAt(a)){case aw:i=Gk;break;default:}break;case Gk:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a-1)),i=Yh,o=a+1;break;case aw:i=lw;break;default:i=UF;break}break;case UF:switch(t.charAt(a)){case aw:i=lw;break;default:}break;default:}if(i===Gk?n.push(t.substring(o,s-1)):n.push(t.substring(o)),e&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var pd=class extends tr{constructor(t){super(),this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new pd(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),sr(n,e)}};var md=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=fi([i,s,u,a],[1,4])}else this.cropBox=fi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new md(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Ay.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ar(Q(t,"float32"),0),n;n=hn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var fd=class{};var Zh=class extends tr{split(t){return new Wk(this,t)}},Wk=class extends Zh{constructor(t,e){super(),this.upstream=t,this.impl=new Uk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Uk=class extends ip{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var uw=class extends tr{decodeUTF8(){return new Hk(this)}},Hk=class extends Zh{constructor(t){super(),this.upstream=t,this.impl=new qk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qk=class extends ip{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=Tk();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var dd=class extends uw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function HF(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=stt(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new dd(i,t)}else throw new Error(s.statusText)}var stt=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function cw(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var hd=class extends fd{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(cw(this.input)&&L().get("IS_NODE")){let t=pw();this.input=t.readFileSync(this.input.slice(7))}return new dd(this.input,this.options)}};var gd=class extends fd{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return cw(this.url)?new hd(this.url,this.fileOptions).iterator():HF(this.url,this.fileOptions)}};function qF(r,t={}){return new cd(new gd(r),t)}function KF(r){let t=Xh(r);return $n(async()=>t)}function jF(r){return $n(async()=>{let t=await r();return Xh(()=>t.next())})}async function XF(r,t){return md.create(r,t)}async function YF(r){return pd.create(r)}var Kk="4.7.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var itt=jr.whereImpl,Xu=class extends Uo{nextDataId(){return Xu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Da(this,Wn())}write(t,e,n){this.firstUse&&(this.firstUse=!1,L().get("IS_NODE")&&S.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. 
============================`));let o={id:this.nextDataId()};return this.data.set(o,{values:t,dtype:n,refCount:1}),o}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return{dataId:o,shape:t,dtype:e}}refCount(t){return this.data.has(t)?this.data.get(t).refCount:0}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,n,o,s){this.data.set(t,{values:e,dtype:o,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:n}=this.data.get(t);if(e==="complex64"){let o=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return S.mergeRealAndImagArrays(o,s)}return y.convertBackendValuesAndArrayBuffer(this.data.get(t).values,e)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}makeOutput(t,e,n){return Wn().makeTensorFromTensorInfo(this.makeTensorInfo(e,n,t),this)}disposeData(t,e=!1){if(this.data.has(t)){if(this.data.get(t).refCount--,!e&&this.data.get(t).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(t);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(t)}return!0}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(t){tt([t],"where");let e=this.readSync(t.dataId);return itt(t.shape,e)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Xu.nextDataId=0;var Nw={};Kt(Nw,{addImpl:()=>Yk,bincountImpl:()=>bd,bincountReduceImpl:()=>mw,bitwiseAndImpl:()=>Zk,castImpl:()=>Xk,ceilImpl:()=>Jk,concatImpl:()=>ap,equalImpl:()=>Qk,expImpl:()=>eT,expm1Impl:()=>nT,floorDivImpl:()=>sT,floorImpl:()=>oT,gatherNdImpl:()=>fw,gatherV2Impl:()=>dw,greaterEqualImpl:()=>aT,greaterImpl:()=>iT,lessEqualImpl:()=>uT,lessImpl:()=>lT,linSpaceImpl:()=>hw,logImpl:()=>cT,maxImpl:()=>gw,maximumImpl:()=>pT,minimumImpl:()=>mT,multiplyImpl:()=>Jh,negImpl:()=>fT,notEqualImpl:()=>dT,prodImpl:()=>hT,raggedGatherImpl:()=>xw,raggedRangeImpl:()=>yw,raggedTensorToTensorImpl:()=>bw,rangeImpl:()=>up,rsqrtImpl:()=>gT,scatterImpl:()=>Si,sigmoidImpl:()=>_O,simpleAbsImpl:()=>jk,sliceImpl:()=>cp,sparseFillEmptyRowsImpl:()=>ww,sparseReshapeImpl:()=>Iw,sparseSegmentReductionImpl:()=>Cd,sqrtImpl:()=>DO,squaredDifferenceImpl:()=>yT,staticRegexReplaceImpl:()=>bT,stridedSliceImpl:()=>Cw,stringNGramsImpl:()=>pp,stringSplitImpl:()=>mp,stringToHashBucketFastImpl:()=>fp,subImpl:()=>IT,tileImpl:()=>vw,topKImpl:()=>Sw,transposeImpl:()=>wd,uniqueImpl:()=>dp});function jk(r){let t=new Float32Array(r.length);for(let e=0;e<r.length;++e)t[e]=Math.abs(r[e]);return t}var att=r=>{let{x:t}=r.inputs,e=r.backend;tt(t,"abs");let n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId).values;return n=jk(o),e.makeOutput(n,t.shape,t.dtype)},JF={kernelName:$i,backendName:"cpu",kernelFunc:att};function Jt(r){return(t,e,n,o,s)=>{let i=S.assertAndGetBroadcastShape(t,e),a=i.length,u=y.computeStrides(i),l=y.sizeFromShape(i),c=y.getTypedArrayFromDType(s,l),p=t.length,m=e.length,f=y.computeStrides(t),d=y.computeStrides(e),h=S.getBroadcastDims(t,i),g=S.getBroadcastDims(e,i);if(h.length+g.length===0)for(let x=0;x<c.length;++x)c[x]=r(n[x%n.length],o[x%o.length]);else for(let x=0;x<c.length;++x){let b=y.indexToLoc(x,a,u),w=b.slice(-p);h.forEach(A=>w[A]=0);let I=y.locToIndex(w,p,f),N=b.slice(-m);g.forEach(A=>N[A]=0);let E=y.locToIndex(N,m,d);c[x]=r(n[I],o[E])}return[c,i]}}function Cr(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=e.makeTensorInfo(n.shape,"complex64"),u=e.data.get(a.dataId);return u.complexTensorInfos={real:e.makeTensorInfo(n.shape,"float32",s),imag:e.makeTensorInfo(o.shape,"float32",i)},a}var QF={kernelName:zp,backendName:"cpu",kernelFunc:Cr};function xd(r,t,e="float32"){if(e==="complex64"){let o=xd(r,t,"float32"),s=xd(r,t,"float32");return Cr({inputs:{real:o,imag:s},backend:r})}let n=y.makeZerosTypedArray(y.sizeFromShape(t),e);return r.makeTensorInfo(t,e,n)}function Zr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var tO={kernelName:bo,backendName:"cpu",kernelFunc:Zr};function Mo(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.real,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var eO={kernelName:Yp,backendName:"cpu",kernelFunc:Mo};function Xk(r,t,e,n){if(n==="int32"){let o=Int32Array.from(r);return[t,"int32",o]}if(n==="bool"){let o=y.toTypedArray([0],e),[s,i]=Jt((a,u)=>a!==u?1:0)(t,[],r,o,"bool");return[i,"bool",s]}throw new Error(`Error in Cast: failed to cast ${e} to ${n}`)}function Lo(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Zr({inputs:{x:o},backend:e});let c=xd(e,o.shape,o.dtype),p=Lo({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),m=Cr({inputs:{real:p,imag:c},backend:e});return e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),m}if(o.dtype==="complex64"){let c=Mo({inputs:{input:o},backend:e}),p=Lo({inputs:{x:c},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(c),p}if(!y.hasEncodingLoss(o.dtype,s)){let c=Zr({inputs:{x:o},backend:e});return{dataId:c.dataId,shape:c.shape,dtype:s}}let i=e.data.get(o.dataId).values,[a,u,l]=Xk(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}var rO={kernelName:xo,backendName:"cpu",kernelFunc:Lo};function oe(r,t,e,n){return e==null?({inputs:o,backend:s})=>{let{a:i,b:a}=o,u=s;tt([i,a],r);let l=u.data.get(i.dataId).values,c=u.data.get(a.dataId).values,p=i.dtype==="string"?S.fromUint8ToStringArray(l):l,m=i.dtype==="string"?S.fromUint8ToStringArray(c):c,f=n||i.dtype,[d,h]=t(i.shape,a.shape,p,m,f);return u.makeTensorInfo(h,f,d)}:({inputs:o,backend:s})=>{let{a:i,b:a}=o,u=s;if(i.dtype==="complex64"||a.dtype==="complex64"){let l=Lo({inputs:{x:i},backend:u,attrs:{dtype:"complex64"}}),c=u.data.get(l.dataId),p=c.complexTensorInfos.real,m=c.complexTensorInfos.imag,f=u.data.get(p.dataId).values,d=u.data.get(m.dataId).values,h=Lo({inputs:{x:a},backend:u,attrs:{dtype:"complex64"}}),g=u.data.get(h.dataId),x=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,w=u.data.get(x.dataId).values,I=u.data.get(b.dataId).values,[N,E,A]=e(i.shape,a.shape,f,d,w,I),D=u.makeTensorInfo(A,"float32",N),F=u.makeTensorInfo(A,"float32",E),P=Cr({inputs:{real:D,imag:F},backend:u});return u.disposeIntermediateTensorInfo(l),u.disposeIntermediateTensorInfo(h),u.disposeIntermediateTensorInfo(D),u.disposeIntermediateTensorInfo(F),P}else{let l=u.data.get(i.dataId).values,c=u.data.get(a.dataId).values,p=n||i.dtype,[m,f]=t(i.shape,a.shape,l,c,p);return u.makeTensorInfo(f,p,m)}}}function yd(r){return(t,e,n,o,s,i)=>{let a=S.assertAndGetBroadcastShape(t,e),u=y.sizeFromShape(a),l=a.length,c=y.computeStrides(a),p=y.getTypedArrayFromDType("float32",u),m=y.getTypedArrayFromDType("float32",u),f=S.getBroadcastDims(t,a),d=S.getBroadcastDims(e,a),h=S.mergeRealAndImagArrays(n,o),g=S.mergeRealAndImagArrays(s,i),x=t.length,b=y.computeStrides(t),w=e.length,I=y.computeStrides(e);if(f.length+d.length===0)for(let N=0;N<p.length;N++){let E=N%h.length,A=N%g.length,D=r(h[E*2],h[E*2+1],g[A*2],g[A*2+1]);p[N]=D.real,m[N]=D.imag}else for(let N=0;N<p.length;N++){let E=y.indexToLoc(N,l,c),A=E.slice(-x);f.forEach(G=>A[G]=0);let D=y.locToIndex(A,x,b),F=E.slice(-w);d.forEach(G=>F[G]=0);let P=y.locToIndex(F,w,I),V=r(h[D*2],h[D*2+1],g[P*2],g[P*2+1]);p[N]=V.real,m[N]=V.imag}return[p,m,a]}}var Yk=Jt((r,t)=>r+t),ltt=yd((r,t,e,n)=>({real:r+e,imag:t+n})),Ca=oe(ao,Yk,ltt),nO={kernelName:ao,backendName:"cpu",kernelFunc:Ca};function bd(r,t,e,n,o){let s=y.sizeFromShape(n),i=y.makeZerosTypedArray(o,e);for(let a=0;a<r.length;a++){let u=r[a];if(u<0)throw new Error("Input x must be non-negative!");u>=o||(s>0?i[u]+=t[a]:i[u]+=1)}return i}function mw(r,t,e,n=!1){let o=r.shape[0],s=r.shape[1],i=wt([o,e],t.dtype);for(let a=0;a<o;a++)for(let u=0;u<s;u++){let l=r.get(a,u);if(l<0)throw new Error("Input x must be non-negative!");l>=e||(n?i.set(1,a,l):t.size>0?i.set(i.get(a,l)+t.get(a,u),a,l):i.set(i.get(a,l)+1,a,l))}return i}var Zk=Jt((r,t)=>r&t),utt=oe(Pa,Zk),oO={kernelName:Pa,backendName:"cpu",kernelFunc:utt};function Er(r){return(t,e,n)=>{let o=y.getArrayFromDType(e,t.length);for(let s=0;s<t.length;++s)o[s]=r(t[s],n);return o}}function At(r,t,e){let n=Er(t);return Rn(r,n,e)}function Rn(r,t,e){return({inputs:n,attrs:o,backend:s})=>{let{x:i}=n;tt(i,r);let a=s,u=a.data.get(i.dataId).values,l;if(i.dtype==="string"){if(!Array.isArray(u))throw new Error("String tensor's value was not an instance of Array");l=S.fromUint8ToStringArray(u)}else l=u;let c=e||i.dtype,p=t(l,c,o);return a.makeTensorInfo(i.shape,c,p)}}var Jk=Er(r=>Math.ceil(r)),ctt=Rn(rs,Jk),sO={kernelName:rs,backendName:"cpu",kernelFunc:ctt};function ap(r,t,e,n){let o=y.getArrayFromDType(e,y.sizeFromShape(t));if(n&&e!=="string"){let s=0;r.forEach(i=>{let a=y.sizeFromShape(i.shape);o.set(i.vals,s),s+=a})}else{let s=0;r.forEach(i=>{let a=e==="string"?S.fromUint8ToStringArray(i.vals):i.vals,u=0;for(let l=0;l<i.shape[0];++l){let c=l*t[1]+s;for(let p=0;p<i.shape[1];++p)o[c+p]=a[u++]}s+=i.shape[1]})}return o}var Qk=Jt((r,t)=>r===t?1:0),tT=oe(Wa,Qk,null,"bool"),iO={kernelName:Wa,backendName:"cpu",kernelFunc:tT};var eT=Er(r=>Math.exp(r)),rT=Rn(ds,eT,"float32"),aO={kernelName:ds,backendName:"cpu",kernelFunc:rT};var nT=Er(r=>Math.expm1(r)),ptt=Rn(hs,nT),lO={kernelName:hs,backendName:"cpu",kernelFunc:ptt};var oT=Er(r=>Math.floor(r)),mtt=Rn(gs,oT),uO={kernelName:gs,backendName:"cpu",kernelFunc:mtt};var sT=Jt((r,t)=>Math.floor(r/t)),ftt=oe(xs,sT,null,"int32"),cO={kernelName:xs,backendName:"cpu",kernelFunc:ftt};function fw(r,t,e,n,o,s,i,a,u){let l=wt([n,s],e);for(let c=0;c<n;c++){let p=[],m=0;for(let f=0;f<o;f++){let d=r[c*o+f];m+=d*i[f],p.push(d)}if(m<0||m>=u/s)throw new Error(`Invalid indices: ${p} does not index into ${a}`);for(let f=0;f<s;f++)l.values[c*s+f]=t.get(...t.indexToLoc(m*s+f))}return l}function dw(r,t,e){let n=wt(e,r.dtype);for(let o=0;o<n.size;++o){let i=n.indexToLoc(o).slice(),a=i[0],u=i[2],l=t.locToIndex([a,u]);i[2]=t.values[l];let c=r.locToIndex(i);0<=c&&c<r.values.length&&(n.values[o]=r.values[c])}return n}var iT=Jt((r,t)=>r>t?1:0),dtt=oe(qa,iT,null,"bool"),pO={kernelName:qa,backendName:"cpu",kernelFunc:dtt};var aT=Jt((r,t)=>r>=t?1:0),htt=oe(bs,aT,null,"bool"),mO={kernelName:bs,backendName:"cpu",kernelFunc:htt};var lT=Jt((r,t)=>r<t?1:0),gtt=oe(Ka,lT,null,"bool"),fO={kernelName:Ka,backendName:"cpu",kernelFunc:gtt};var uT=Jt((r,t)=>r<=t?1:0),xtt=oe(ja,uT,null,"bool"),dO={kernelName:ja,backendName:"cpu",kernelFunc:xtt};function hw(r,t,e){let n=(t-r)/(e-1),o=y.makeZerosTypedArray(e,"float32");o[0]=r;for(let s=1;s<o.length;s++)o[s]=o[s-1]+n;return o}var cT=Er(r=>Math.log(r)),ytt=Rn(Ss,cT),hO={kernelName:Ss,backendName:"cpu",kernelFunc:ytt};function gw(r,t,e,n){let o=y.getTypedArrayFromDType(n,y.sizeFromShape(e));for(let s=0;s<o.length;++s){let i=s*t,a=r[i];for(let u=0;u<t;++u){let l=r[i+u];(Number.isNaN(l)||l>a)&&(a=l)}o[s]=a}return o}var pT=Jt((r,t)=>Math.max(r,t)),btt=oe(_s,pT),gO={kernelName:_s,backendName:"cpu",kernelFunc:btt};var mT=Jt((r,t)=>Math.min(r,t)),wtt=oe($s,mT),xO={kernelName:$s,backendName:"cpu",kernelFunc:wtt};var Jh=Jt((r,t)=>r*t),Itt=yd((r,t,e,n)=>({real:r*e-t*n,imag:r*n+t*e})),lp=oe(Os,Jh,Itt),yO={kernelName:Os,backendName:"cpu",kernelFunc:lp};function fT(r,t,e){let n=y.createScalarValue(-1,e);return Jh([],t,n,r,e)}function Ctt(r){let{inputs:t,backend:e}=r,{x:n}=t;tt(n,"neg");let o=e.data.get(n.dataId).values,[s,i]=fT(o,n.shape,n.dtype);return e.makeTensorInfo(i,n.dtype,s)}var bO={kernelName:Vi,backendName:"cpu",kernelFunc:Ctt};var dT=Jt((r,t)=>r!==t?1:0),vtt=oe(el,dT,null,"bool"),wO={kernelName:el,backendName:"cpu",kernelFunc:vtt};function wd(r,t,e,n,o){let s=t.length,i=y.sizeFromShape(t),a=y.computeStrides(t),u=y.computeStrides(o),l=y.getTypedArrayFromDType(e,y.sizeFromShape(o));for(let c=0;c<i;++c){let p=y.indexToLoc(c,s,a),m=new Array(p.length);for(let d=0;d<m.length;d++)m[d]=p[n[d]];let f=y.locToIndex(m,s,u);l[f]=r[c]}return l}function Ge(r){let{inputs:t,attrs:e,backend:n}=r,{x:o}=t,{perm:s}=e;tt(o,"transpose");let i=o.shape.length,a=new Array(i);for(let p=0;p<a.length;p++)a[p]=o.shape[s[p]];let u=n.data.get(o.dataId).values,l=wd(u,o.shape,o.dtype,s,a);return{dataId:n.write(l,a,o.dtype),shape:a,dtype:o.dtype}}var IO={kernelName:uo,backendName:"cpu",kernelFunc:Ge};function hT(r,t,e,n){let[o,s]=S.computeOutAndReduceShapes(r,n),i=ur(t,"int32"),a=y.makeZerosTypedArray(y.sizeFromShape(o),i),u=y.sizeFromShape(s);for(let l=0;l<a.length;++l){let c=l*u,p=1;for(let m=0;m<u;++m)p*=e[c+m];a[l]=p}return{outVals:a,outShape:o,outDtype:i}}function Stt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"prod");let a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=S.getAxesPermutation(u,a),c=u,p=o,m=[];l!=null&&(p=Ge({inputs:{x:o},backend:e,attrs:{perm:l}}),m.push(p),c=S.getInnerMostAxes(c.length,a));let f=e.data.get(p.dataId).values,{outVals:d,outShape:h,outDtype:g}=hT(p.shape,p.dtype,f,c),x=h;return i&&(x=S.expandShapeToKeepDim(h,u)),m.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.makeTensorInfo(x,g,d)}var CO={kernelName:Bs,backendName:"cpu",kernelFunc:Stt};function Ntt(r,t,e){r.forEach((n,o)=>{if(n<0||n>=e){let s=y.indexToLoc(o,t.length,y.computeStrides(t)).join(",");throw new Error(`indices[${s}] = ${n} is not in [0, ${e})`)}})}function ktt(r,t){for(let e=0;e<r.length;++e){let n=r[e],o=e===r.length-1?t:r[e+1].length;if(n.length===0)throw new Error("Ragged splits may not be empty");if(n[0]<0)throw new Error("Ragged splits must be non-negative");if(n[n.length-1]>o)throw new Error("Ragged splits must not point past values");for(let s=1;s<n.length;++s)if(n[s-1]>n[s])throw new Error("Ragged splits must be sorted in ascending order")}}function Ttt(r,t,e,n){let o=[],s=0,i=t.length-1+e.length,a=new Array(i).fill(null).map(()=>[0]);ktt(e,n);let u=1;for(let l=0;l<t.length-1;++l){u*=t[l];let c=t[l+1];for(let p=1;p<u+1;++p)a[l].push(p*c)}for(let l=0;l<r.length;++l){let c=r[l],p=r[l]+1;for(let m=0;m<e.length;++m){let f=e[m],d=m+t.length-1;if(d>=0){let h=a[d],g=h[h.length-1]-f[c];for(let x=c;x<p;++x)a[d].push(f[x+1]+g)}c=f[c],p=f[p]}p!==c&&(o.push([c,p]),s+=p-c)}return{outSplits:a,valueSlices:o,numValues:s}}function _tt(r){let t=[];for(let e=0;e<r.length;++e){let n=r[e].length,o=y.getArrayFromDType("int32",n);t.push(o),r[e].forEach((s,i)=>o[i]=s)}return t}function vO(r,t){let e=r.slice(0,t);for(;e.length<t;)e.push(1);for(let n=t;n<r.length;n++)e[t-1]*=r[n];return e}function Ett(r,t,e,n,o,s){let i=vO(t,2)[1],a=vO(s,2)[1],u=0;for(let l of e)for(let c=l[0];c<l[1];++c){for(let p=0;p<n;++p)o[u*a+p]=r[c*i+p];++u}}function Att(r,t,e,n,o){let s=t.slice();s[0]=o;let i=y.getArrayFromDType(e,y.sizeFromShape(s)),a=r.length,u=a===0?0:a/t[0];return Ett(r,t,n,u,i,s),[i,s]}function xw(r,t,e,n,o,s,i,a){if(r.length===0)throw new Error("paramsNestedSplits must be non empty");if(t[0].length===0)throw new Error("Split tensors must not be scalars");let u=t[0][0]-1;if(Ntt(s,i,u),n.length===0)throw new Error("params.rank must be nonzero");let l=n[0],{outSplits:c,valueSlices:p,numValues:m}=Ttt(s,i,r,l),f=_tt(c),d=Att(e,n,o,p,m);return[f,d[0],d[1]]}var SO=2147483647;function yw(r,t,e,n,o,s,i){if(t.length>1)throw new Error("starts must be a scalar or vector");if(o.length>1)throw new Error("limits must be a scalar or vector");if(i.length>1)throw new Error("deltas must be a scalar or vector");let a=t.length===0,u=o.length===0,l=i.length===0,c=[];a||c.push(t[0]),u||c.push(o[0]),l||c.push(i[0]);for(let g=1;g<c.length;++g)if(c[g]!==c[g-1])throw new Error("starts, limits, and deltas must have the same shape");let p=c.length===0?1:c[0],m=y.getArrayFromDType("int32",p+1);m[0]=0;for(let g=0;g<p;++g){let x=a?r[0]:r[g],b=u?n[0]:n[g],w=l?s[0]:s[g];if(w===0)throw new Error("Requires delta != 0");let I;if(w>0&&b<x||w<0&&b>x)I=0;else if(I=Math.ceil(Math.abs((b-x)/w)),I>SO)throw new Error(`Requires ((limit - start) / delta) <= ${SO}`);m[g+1]=m[g]+I}let f=m[p],d=y.getArrayFromDType(e,f),h=0;for(let g=0;g<p;++g){let x=m[g+1]-m[g],b=a?r[0]:r[g],w=l?s[0]:s[g];for(let I=0;I<x;++I)d[h++]=b,b+=w}return[m,d]}var zo=S.RowPartitionType,Id=class{constructor(t,e,n,o,s,i,a,u,l,c){this.shape=t,this.shapeShape=e,this.values=n,this.valuesShape=o,this.valuesDType=s,this.defaultValue=i,this.defaultValueShape=a,this.rowPartitionValues=u,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=S.getRowPartitionTypesHelper(c),this.raggedRank=S.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(t){return this.rowPartitionTypes[0]===zo.FIRST_DIM_SIZE?this.rowPartitionTypes[t+1]:this.rowPartitionTypes[t]}getRowPartitionTensor(t){return this.rowPartitionTypes[0]===zo.FIRST_DIM_SIZE?this.rowPartitionValues[t+1]:this.rowPartitionValues[t]}getMaxWidth(t){let e=this.getRowPartitionTensor(t-1);switch(this.getRowPartitionTypeByDimension(t-1)){case zo.VALUE_ROWIDS:return Id.getMaxWidthValueRowID(e);case zo.ROW_SPLITS:return Id.getMaxWidthRowSplit(e);default:throw new Error(`Cannot handle partition type ${zo[this.getRowPartitionTypeByDimension(t-1)]}`)}}static getMaxWidthRowSplit(t){let e=t.length;if(e===0||e===1)return 0;let n=0;for(let o=0;o<e-1;++o){let s=t[o+1]-t[o];s>n&&(n=s)}return n}static getMaxWidthValueRowID(t){let e=t.length;if(e===0)return 0;let n=0,o=t[0],s=0;for(let i=1;i<e;++i){let a=t[i];a!==o&&(o=a,s=Math.max(i-n,s),n=i)}return Math.max(e-n,s)}tensorShapeFromTensor(t,e,n=!0){if(e.length===0){if(t[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return kO(t,n)}calculateOutputSize(t){let e=this.valuesShape,n=this.defaultValueShape;S.validateDefaultValueShape(n,e);let o=this.tensorShapeFromTensor(this.shape,this.shapeShape),i=S.combineRaggedTensorToTensorShapes(this.raggedRank,o,e);i[0]<0&&(i[0]=t);for(let a=1;a<=this.raggedRank;++a)i[a]<0&&(i[a]=this.getMaxWidth(a));return i}calculateFirstParentOutputIndex(t,e,n){let o=Math.min(t,n),s=[],i=0;for(let a=0;a<o;++a,i+=e)s.push(i);for(let a=o;a<t;++a)s.push(-1);return y.assert(s.length===t,()=>"Final length of result must be equal to firstDimension."),s}calculateOutputIndexRowSplit(t,e,n,o){let s=t.length,i=[];for(let a=0;a<s-1;++a){let u=t[a+1]-t[a],l=Math.min(o,u),c=e[a];c===-1&&(l=0);for(let p=0;p<l;++p)i.push(c),c+=n;for(let p=0;p<u-l;++p)i.push(-1)}if(s>0&&i.length!==t[s-1])throw new Error("Invalid row split size.");return i}calculateOutputIndexValueRowID(t,e,n,o){let s=t.length,i=[];if(s===0)return[];let a=0,u=t[0];if(u>=e.length)throw new Error(`Got currentValueRowId=${u}, which is not less than ${e.length}`);let l=e[u];i.push(l);for(let c=1;c<s;++c){let p=t[c];if(p===u)l>=0&&(++a,a<o?l+=n:l=-1);else{if(a=0,u=p,p>=e.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${e.length}`);l=e[p]}i.push(l)}if(i.length!==t.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(t,e,n,o){let s=this.getRowPartitionTensor(t),i=this.getRowPartitionTypeByDimension(t);switch(i){case zo.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,e,n,o);case zo.ROW_SPLITS:if(s.length-1>e.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${e.length}`);return this.calculateOutputIndexRowSplit(s,e,n,o);default:throw new Error(`Unsupported partition type: ${zo[i]}`)}}getFirstDimensionSize(){let t=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let e=this.rowPartitionTypes[0];switch(e){case zo.FIRST_DIM_SIZE:return t[0];case zo.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case zo.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${zo[e]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),n=this.calculateOutputSize(e),o=new Array(this.raggedRank+1);o[o.length-1]=1;for(let u=o.length-2;u>=0;--u)o[u]=o[u+1]*n[u+1];let s=kO(n,!1),i=y.getArrayFromDType(this.valuesDType,y.sizeFromShape(s));if(o[0]*n[0]>0){let u=this.calculateFirstParentOutputIndex(e,o[0],n[0]);for(let l=1;l<=this.raggedRank;++l)u=this.calculateOutputIndex(l-1,u,o[l],n[l]);this.setOutput(this.raggedRank,u,i,s)}return[s,i]}setOutput(t,e,n,o){if(n.length===0)return;let s=this.values,i=n,a=o.slice();a=a.slice(t+1);let u=y.sizeFromShape(a),l=e.length,c=this.defaultValue;if(c.length!==u&&c.length!==1){let d=this.defaultValueShape;B(()=>{let h=R(c,d);c=la(h,a).dataSync()})}let p=0,m=0,f=0;for(let d=0;d<=l;++d){let h=d<l?e[d]:-1;if(h===f){++f;continue}if(m<f){let g=s.subarray(p*u),x=i.subarray(m*u),b=(f-m)*u;NO(x,g,b)}if(d>=l){let g=n.length;h=Math.floor(g/u)}if(h>f)if(this.defaultValue.length===1)i.subarray(f*u,h*u).fill(this.defaultValue[0]),f=h;else for(;h>f;){let g=i.slice(f*u);NO(g,c,u),++f}h<0?(p=d+1,m=f):(p=d,m=f,f=m+1)}}};function NO(r,t,e){for(let n=0;n<e;n++)r[n]=t[n]}function kO(r,t){let e=[];for(let n of r){if(n<0){if(!t)throw new Error(`Dimension ${n} must be >= 0`);if(n<-1)throw new Error(`Dimension ${n} must be >= -1`);n=-1}e.push(n)}return e}function bw(r,t,e,n,o,s,i,a,u,l){return new Id(r,t,e,n,o,s,i,a,u,l).compute()}function up(r,t,e,n){let o=r===t,s=r<t&&e<0,i=t<r&&e>1;if(o||s||i)return y.makeZerosTypedArray(0,n);let a=Math.abs(Math.ceil((t-r)/e)),u=y.makeZerosTypedArray(a,n);t<r&&e===1&&(e=-1),u[0]=r;for(let l=1;l<u.length;l++)u[l]=u[l-1]+e;return u}var gT=Er(r=>1/Math.sqrt(r)),Dtt=Rn(js,gT),TO={kernelName:js,backendName:"cpu",kernelFunc:Dtt};function Si(r,t,e,n,o,s,i,a,u,l){let c=[n/o,o],p=r.values,m=t.values;if(n===0)return wt(e,t.dtype);let f=u instanceof le?u:wt(c,t.dtype);typeof u=="string"||typeof u=="number"?f.values.fill(u):typeof u=="boolean"&&f.values.fill(+u);for(let d=0;d<s;d++){let h=[],g=0;for(let x=0;x<i;x++){let b=p[d*i+x];h.push(b),g+=b*a[x]}if(g<0||g>=n/o)throw new Error(`Invalid indices: ${h} does not index into ${e}`);for(let x=0;x<o;x++)l?f.values[g*o+x]+=m[d*o+x]:f.values[g*o+x]=t.rank===0?m[0]:m[d*o+x]}return f}var _O=Er(r=>1/(1+Math.exp(-r))),xT=At(Qs,r=>1/(1+Math.exp(-r))),EO={kernelName:Qs,backendName:"cpu",kernelFunc:xT};function cp(r,t,e,n,o){let s=ze.isSliceContinous(n,t,e),i=y.sizeFromShape(e),a=y.computeStrides(n);if(s){let p=ze.computeFlatOffset(t,a);return o==="string"?r.slice(p,p+i):r.subarray(p,p+i)}let u=o==="string"?S.fromUint8ToStringArray(r):r,l=wt(n,o,u),c=wt(e,o);for(let p=0;p<c.size;++p){let m=c.indexToLoc(p),f=m.map((d,h)=>d+t[h]);c.set(l.get(...f),...m)}return o==="string"?S.fromStringArrayToUint8(c.values):c.values}function Bo(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n;tt(o,"slice");let[a,u]=ze.parseSliceParams(o,s,i);ze.assertParamsValid(o,a,u);let l=e.data.get(o.dataId).values,c=cp(l,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,c)}var AO={kernelName:qi,backendName:"cpu",kernelFunc:Bo};function ww(r,t,e,n,o,s,i){let a=t[0],u=s[0],l=new Array(u),c=new Array(a),p=t[1];if(u===0){if(a!==0)throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(a));let g=y.getArrayFromDType(e,0),x=y.getArrayFromDType(o,0);return[g,[0,p],x,l,c]}let m=!0,f=0,d=new Array(u).fill(0);for(let g=0;g<a;++g){let x=r[g*p];if(x<0)throw new Error(S.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,x));if(x>=u)throw new Error(S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,x,u));++d[x],m=m&&x>=f,f=x}let h=!0;for(let g=0;g<u;++g){let x=d[g]===0;l[g]=x,h=h&&!x,d[g]=Math.max(d[g],1),g>0&&(d[g]+=d[g-1])}if(h&&m){let g=r,x=n;for(let b=0;b<a;++b)c[b]=b;return[g,[a,p],x,l,c]}else{let g=d[u-1],x=y.getArrayFromDType(e,g*p),b=y.getArrayFromDType(o,g),w=new Array(u).fill(0);for(let I=0;I<a;++I){let N=r[I*p],E=w[N],A=(N===0?0:d[N-1])+E;w[N]++;for(let D=0;D<p;++D)x[A*p+D]=r[I*p+D];b[A]=n[I],c[I]=A}for(let I=0;I<u;++I)if(w[I]===0){let E=I===0?0:d[I-1];x[E*p+0]=I;for(let A=1;A<p;++A)x[E*p+A]=0;b[E]=i}return[x,[g,p],b,l,c]}}function Iw(r,t,e,n,o){let s=y.sizeFromShape(n),i=t[0],a=o.length,u=[],l=1,c=-1;for(let g=0;g<a;++g){let x=o[g];if(x===-1){if(c!==-1)throw new Error(S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c,g));c=g,u.push(1)}else{if(x<0)throw new Error(S.getSparseReshapeNegativeOutputDimErrorMessage(g,x));l*=x,u.push(x)}}if(c!==-1){if(l<=0)throw new Error(S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let g=Math.trunc(s/l);if(l*g!==s)throw new Error(S.getSparseReshapeInputOutputMultipleErrorMessage(n,u));u[c]=g}if(y.sizeFromShape(u)!==s)throw new Error(S.getSparseReshapeInputOutputMismatchErrorMessage(n,u));let m=n.length,f=[];if(m>0){f[m-1]=1;for(let g=m-2;g>=0;--g)f[g]=f[g+1]*n[g+1]}let d=[];if(a>0){d[a-1]=1;for(let g=a-2;g>=0;--g)d[g]=d[g+1]*u[g+1]}let h=y.getArrayFromDType(e,i*a);for(let g=0;g<i;++g){let x=0;for(let b=0;b<m;++b)x+=r[g*m+b]*f[b];for(let b=0;b<a;++b)h[g*a+b]=Math.trunc(x/d[b]),x%=d[b]}return[h,[i,a],u]}function Cd(r,t,e,n,o,s=!1,i=0){let a=n.length,u=[t[0],r.length/t[0]],l=u[1],p=a>0?o[a-1]+1:0;if(p<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=t.slice();m[0]=p;let f=m.reduce((w,I)=>w*I,1),d=y.getArrayFromDType(e,f);if(a===0)return p>0&&d.fill(i),[d,m];if(p<=0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let h=0,g=1,x=0,b=o[h];for(;;){let w=0;if(g<a){if(w=o[g],b===w){++g;continue}if(b>=w)throw new Error(S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=p)throw new Error(S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,p));b>x&&d.fill(i,x*l,b*l);for(let I=h;I<g;++I){let N=n[I];if(N<0||N>=u[0])throw new Error(S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(I,n[I],u[0]));for(let E=0;E<l;E++)d[b*l+E]+=r[N*l+E]}if(s)for(let I=0;I<l;I++)d[b*l+I]/=g-h;if(h=g,++g,x=b+1,b=w,g>a)break}return x<p&&d.fill(i,x*l,p*l),[d,m]}var DO=Er(r=>Math.sqrt(r)),$tt=At(ei,r=>Math.sqrt(r)),$O={kernelName:ei,backendName:"cpu",kernelFunc:$tt};var yT=Jt((r,t)=>{let e=r-t;return e*e}),Rtt=oe(oi,yT),RO={kernelName:oi,backendName:"cpu",kernelFunc:Rtt};var bT=Er((r,t)=>{let{pattern:e,replaceGlobal:n,rewrite:o}=t;return r.replace(new RegExp(e,n?"g":""),o)}),Ftt=Rn(cc,bT),FO={kernelName:cc,backendName:"cpu",kernelFunc:Ftt};function Cw(r,t,e,n){let o=wt(r,t.dtype);for(let s=0;s<o.size;s++){let i=o.indexToLoc(s),a=new Array(i.length);for(let u=0;u<a.length;u++)a[u]=i[u]*e[u]+n[u];o.set(t.get(...a),...i)}return o}var wT=class{constructor(t,e,n,o,s,i){this.separator=y.encodeString(t),this.nGramWidths=e,this.leftPad=y.encodeString(n),this.rightPad=y.encodeString(o),this.padWidth=s,this.preserveShort=i}getPadWidth(t){return Math.min(this.padWidth<0?t-1:this.padWidth,t-1)}getNumNGrams(t,e){let n=this.getPadWidth(e);return Math.max(0,t+2*n-e+1)}createNGrams(t,e,n,o,s,i){for(let a=0;a<s;++a){let u=this.getPadWidth(i),l=Math.max(0,u-a),c=Math.max(0,u-(s-(a+1))),p=i-(l+c),m=e+(l>0?0:a-u),f=0;f+=l*this.leftPad.length;for(let b=0;b<p;++b)f+=t[m+b].length;f+=c*this.rightPad.length;let d=l+c+p-1;f+=d*this.separator.length,n[o+a]=new Uint8Array(f);let h=n[o+a],g=0,x=b=>b.forEach(w=>h[g++]=w);for(let b=0;b<l;++b)x(this.leftPad),x(this.separator);for(let b=0;b<p-1;++b)x(t[m+b]),x(this.separator);if(p>0){x(t[m+p-1]);for(let b=0;b<c;++b)x(this.separator),x(this.rightPad)}else{for(let b=0;b<c-1;++b)x(this.rightPad),x(this.separator);x(this.rightPad)}}}compute(t,e){let n=t.length,o=e.length;if(o>0){let u=e[0];if(u!==0)throw new Error(`First split value must be 0, got ${u}`);for(let l=1;l<o;++l){let c=e[l]>=u;if(c=c&&e[l]<=n,!c)throw new Error(`Invalid split value ${e[l]}, must be in [${u}, ${n}]`);u=e[l]}if(u!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${u}`)}let s=o-1,i=y.getArrayFromDType("int32",o);if(n===0||o===0){let u=new Array(n);for(let l=0;l<=s;++l)i[l]=0;return[u,i]}i[0]=0;for(let u=1;u<=s;++u){let l=e[u]-e[u-1],c=0;this.nGramWidths.forEach(p=>{c+=this.getNumNGrams(l,p)}),this.preserveShort&&l>0&&c===0&&(c=1),i[u]=i[u-1]+c}let a=new Array(i[s]);for(let u=0;u<s;++u){let l=e[u],c=i[u];if(this.nGramWidths.forEach(p=>{let m=e[u+1]-e[u],f=this.getNumNGrams(m,p);this.createNGrams(t,l,a,c,f,p),c+=f}),this.preserveShort&&c===i[u]){let p=e[u+1]-e[u];if(p===0)continue;let m=p+2*this.padWidth,f=1;this.createNGrams(t,l,a,c,f,m)}}return[a,i]}};function pp(r,t,e,n,o,s,i,a){return new wT(e,n,o,s,i,a).compute(r,t)}function Ott(r,t,e,n){if(!r.length)return;if(t.length===0){for(let s=0;s<r.length;++s)n.push(r.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=r.indexOf(s);for(;i!==-1;){let a=r.subarray(0,i);(!e||a.length!==0)&&n.push(a),r=r.subarray(i+1),i=r.indexOf(s)}(!e||r.length!==0)&&n.push(r);return}let o=0;for(let s=0;s<r.length+1;s++)if(s===r.length||t.indexOf(r[s])!==-1){let i=r.subarray(o,s);(!e||i.length!==0)&&n.push(i),o=s+1}}function mp(r,t,e){let n=r.length,o=[],s=0,i=0,a=new Array(n);for(let m=0;m<n;++m){let f=o.length;Ott(r[m],t,e,o);let d=o.length-f;a[m]=d,s+=d,i=Math.max(i,d)}let u=y.getArrayFromDType("int32",s*2),l=new Array(s),c=[n,i],p=0;for(let m=0;m<n;++m)for(let f=0;f<a[m];++f)u[p*2]=m,u[p*2+1]=f,l[p]=o[p],++p;return[u,l,c]}function fp(r,t){let e=y.getArrayFromDType("int32",r.length);for(let n=0;n<r.length;++n)e[n]=y.fingerPrint64(r[n]).modulo(t).getLowBitsUnsigned();return e}var IT=Jt((r,t)=>r-t),Ptt=yd((r,t,e,n)=>({real:r-e,imag:t-n})),Qh=oe(si,IT,Ptt),OO={kernelName:si,backendName:"cpu",kernelFunc:Qh};function vw(r,t){let e=new Array(r.rank);for(let o=0;o<e.length;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.values,mt=e.blockSize;for(let gt=0;gt<V;gt++){let Ct=gt%g,Rt=gt%x;for(let Dt=0;Dt<F;Dt+=mt){let Ht=Math.min(Dt+mt,F);for(let qt=0;qt<P;qt+=mt){let ce=Math.min(qt+mt,P);for(let ge=0;ge<D;ge+=mt){let re=Math.min(ge+mt,D);for(let xe=Dt;xe<Ht;xe++)for(let fe=qt;fe<ce;fe++){let Ae=0;for(let De=ge;De<re;De++){let Ln=G[Ct*K+xe*X+De*Z],lr=W[De*et+fe*nt+Rt*st];Ae+=Ln*lr}it[gt*at+(xe*P+fe)]+=Ae}}}}}return e.disposeIntermediateTensorInfo(E),e.disposeIntermediateTensorInfo(A),e.makeTensorInfo(w,ot.dtype,ot.values)}var UO={kernelName:es,backendName:"cpu",kernelFunc:TT};function Ltt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m,f,d,h=[];m=TT({inputs:{a:o,b:s},attrs:{transposeA:u,transposeB:l},backend:e}),i&&(f=Ca({inputs:{a:m,b:i},backend:e}),h.push(m),m=f),c&&(d=hp(e,m,c,a,p),h.push(m),m=d);for(let x of h)e.disposeIntermediateTensorInfo(x);return m}var HO={kernelName:Zi,backendName:"cpu",kernelFunc:Ltt};var ztt=At(qo,r=>Math.acos(r)),qO={kernelName:qo,backendName:"cpu",kernelFunc:ztt};var Btt=At(Ko,r=>Math.acosh(r)),KO={kernelName:Ko,backendName:"cpu",kernelFunc:Btt};function Vtt(r){let{inputs:t,backend:e}=r,n=t;tt(t,"addN");let o=n.map(a=>e.data.get(a.dataId).values),s=wt(n[0].shape,n[0].dtype),i=s.values;for(let a=0;a<n.length;a++){let u=o[a];for(let l=0;l<i.length;l++)i[l]+=u[l]}return e.makeTensorInfo(s.shape,s.dtype,s.values)}var jO={kernelName:jo,backendName:"cpu",kernelFunc:Vtt};function Gtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"all");let a=y.parseAxisParam(s,o.shape),u=a,l=S.getAxesPermutation(u,o.shape.length),c=o;l!=null&&(c=Ge({inputs:{x:o},backend:e,attrs:{perm:l}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("all",u,c.shape.length);let[p,m]=S.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=e.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let I=0;I<f;++I){let N=h[b+I];w=w&&N}d[x]=w}l!=null&&e.disposeIntermediateTensorInfo(c);let g=e.makeTensorInfo(p,c.dtype,d);if(i){let x=S.expandShapeToKeepDim(p,a),b=Qt({inputs:{x:g},backend:e,attrs:{shape:x}});return e.disposeIntermediateTensorInfo(g),b}return g}var XO={kernelName:Ra,backendName:"cpu",kernelFunc:Gtt};function Wtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"any");let a=y.parseAxisParam(s,o.shape),u=a,l=S.getAxesPermutation(u,o.shape.length),c=o;l!=null&&(c=Ge({inputs:{x:o},backend:e,attrs:{perm:l}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("any",u,c.shape.length);let[p,m]=S.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=e.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let I=0;I<f;++I){let N=h[b+I];w=w||N}d[x]=w}l!=null&&e.disposeIntermediateTensorInfo(c);let g=e.makeTensorInfo(p,c.dtype,d);if(i){let x=S.expandShapeToKeepDim(p,a),b=Qt({inputs:{x:g},backend:e,attrs:{shape:x}});return e.disposeIntermediateTensorInfo(g),b}return g}var YO={kernelName:Fa,backendName:"cpu",kernelFunc:Wtt};function Utt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n;tt(o,"argMax");let i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Ge({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],S.assertAxesAreInnerMostDims("argMax",i,u.shape.length);let[c,p]=S.computeOutAndReduceShapes(u.shape,i),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=e.data.get(u.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let I=0;I<d;++I){let N=h[x+I];N>b&&(b=N,w=I)}f[g]=w}return l.forEach(g=>e.disposeIntermediateTensorInfo(g)),e.makeTensorInfo(c,"int32",f)}var ZO={kernelName:Ri,backendName:"cpu",kernelFunc:Utt};function Htt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n;tt(o,"argMin");let i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Ge({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],S.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[c,p]=S.computeOutAndReduceShapes(u.shape,i),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=e.data.get(u.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let I=0;I<d;++I){let N=h[x+I];N<b&&(b=N,w=I)}f[g]=w}return l.forEach(g=>e.disposeIntermediateTensorInfo(g)),e.makeTensorInfo(c,"int32",f)}var JO={kernelName:Fi,backendName:"cpu",kernelFunc:Htt};var qtt=At(Xo,r=>Math.asin(r)),QO={kernelName:Xo,backendName:"cpu",kernelFunc:qtt};var Ktt=At(Yo,r=>Math.asinh(r)),tP={kernelName:Yo,backendName:"cpu",kernelFunc:Ktt};var jtt=At(Zo,r=>Math.atan(r)),eP={kernelName:Zo,backendName:"cpu",kernelFunc:jtt};var Xtt=Jt((r,t)=>Math.atan2(r,t)),Ytt=oe(Qo,Xtt),rP={kernelName:Qo,backendName:"cpu",kernelFunc:Ytt};var Ztt=At(Jo,r=>Math.atanh(r)),nP={kernelName:Jo,backendName:"cpu",kernelFunc:Ztt};function vd(r,t,e,n,o,s){let i=o.strideHeight,a=o.strideWidth,u=o.dilationHeight,l=o.dilationWidth,c=o.effectiveFilterHeight,p=o.effectiveFilterWidth,m=o.padInfo.top,f=o.padInfo.left,d=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=wt(o.outShape,e),g=h.values,x=o.outShape[1]*o.outShape[2]*o.outShape[3],b=o.outShape[2]*o.outShape[3],w=o.outShape[3];for(let I=0;I<o.batchSize;++I){let N=I*x,E=I*n[0];for(let A=0;A<o.inChannels;++A)for(let D=0;D<o.outHeight;++D){let F=D*i-m,P=Math.max(0,F),V=Math.min(o.inHeight,c+F),G=N+D*b;for(let W=0;W<o.outWidth;++W){let q=W*a-f,H=Math.max(0,q),K=Math.min(o.inWidth,p+q),X=d,Z=0,et=0;for(let st=P;st<V;st+=u){let at=E+st*n[1];for(let ot=H;ot<K;ot+=l){let it=at+ot*n[2],mt=r[it+A];s==="max"&&mt>X?X=mt:s==="avg"&&(Z+=mt,et++)}if(isNaN(X))break}let nt=G+W*w+A;g[nt]=s==="avg"?Z/et:X}}}return h}function kw(r,t,e,n,o=!1,s=!1){let i=wt(n.outShape,"int32"),a=n.strideHeight,u=n.strideWidth,l=n.dilationHeight,c=n.dilationWidth,p=n.effectiveFilterHeight,m=n.effectiveFilterWidth,f=n.padInfo.top,d=n.padInfo.left,h=wt(t,e,r);for(let g=0;g<n.batchSize;++g)for(let x=0;x<n.inChannels;++x)for(let b=0;b<n.outHeight;++b){let w=b*a-f,I=w;for(;I<0;)I+=l;let N=Math.min(n.inHeight,p+w);for(let E=0;E<n.outWidth;++E){let A=E*u-d,D=A;for(;D<0;)D+=c;let F=Math.min(n.inWidth,m+A),P=Number.NEGATIVE_INFINITY,V=-1;for(let G=I;G<N;G+=l){let W=G-w;for(let q=D;q<F;q+=c){let H=q-A,K=h.get(g,G,q,x);K>P&&(P=K,o?V=s?((g*n.inHeight+G)*n.inWidth+q)*n.inChannels+x:(G*n.inWidth+q)*n.inChannels+x:V=W*m+H)}}i.set(V,g,b,E,x)}}return i}function Tw(r,t,e,n,o,s){let i=o.strideDepth,a=o.strideHeight,u=o.strideWidth,l=o.dilationDepth,c=o.dilationHeight,p=o.dilationWidth,m=o.effectiveFilterDepth,f=o.effectiveFilterHeight,d=o.effectiveFilterWidth,h=o.padInfo.front,g=o.padInfo.top,x=o.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=wt(o.outShape,e),I=w.values,N=o.outShape[1]*o.outShape[2]*o.outShape[3]*o.outShape[4],E=o.outShape[2]*o.outShape[3]*o.outShape[4],A=o.outShape[3]*o.outShape[4],D=o.outShape[4];for(let F=0;F<o.batchSize;++F){let P=F*N,V=F*n[0];for(let G=0;G<o.inChannels;++G)for(let W=0;W<o.outDepth;++W){let q=W*i-h,H=q;for(;H<0;)H+=l;let K=Math.min(o.inDepth,m+q),X=P+W*E;for(let Z=0;Z<o.outHeight;++Z){let et=Z*a-g,nt=et;for(;nt<0;)nt+=c;let st=Math.min(o.inHeight,f+et),at=X+Z*A;for(let ot=0;ot<o.outWidth;++ot){let it=ot*u-x,mt=it;for(;mt<0;)mt+=p;let gt=Math.min(o.inWidth,d+it),Ct=at+ot*D,Rt=b,Dt=0,Ht=0;for(let ce=H;ce<K;ce+=l){let ge=V+ce*n[1];for(let re=nt;re<st;re+=c){let xe=ge+re*n[2];for(let fe=mt;fe<gt;fe+=p){let Ae=xe+fe*n[3],De=r[Ae+G];if(s==="max"&&De>Rt?Rt=De:s==="avg"&&(Dt+=De,Ht++),isNaN(Rt))break}if(isNaN(Rt))break}if(isNaN(Rt))break}let qt=Ct+G;I[qt]=s==="avg"?Dt/Math.max(Ht,1):Rt}}}}return w}function oP(r,t){let e=wt(t.outShape,"int32"),n=t.strideDepth,o=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,a=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,m=t.padInfo.front,f=t.padInfo.top,d=t.padInfo.left;for(let h=0;h<t.batchSize;++h)for(let g=0;g<t.inChannels;++g)for(let x=0;x<t.outDepth;++x){let b=x*n-m,w=b;for(;w<0;)w+=i;let I=Math.min(t.inDepth,l+b);for(let N=0;N<t.outHeight;++N){let E=N*o-f,A=E;for(;A<0;)A+=a;let D=Math.min(t.inHeight,c+E);for(let F=0;F<t.outWidth;++F){let P=F*s-d,V=P;for(;V<0;)V+=u;let G=Math.min(t.inWidth,p+P),W=Number.NEGATIVE_INFINITY,q=-1;for(let H=w;H<I;H+=i){let K=H-b;for(let X=A;X<D;X+=a){let Z=X-E;for(let et=V;et<G;et+=u){let nt=et-P,st=r.get(h,H,X,et,g);st>=W&&(W=st,q=K*c*p+Z*c+nt)}}}e.set(q,h,x,N,F,g)}}}return e}function Jtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Zr({inputs:{x:o},backend:e});else{let m=e.data.get(o.dataId).values,f=y.computeStrides(o.shape),d=vd(m,o.shape,o.dtype,f,c,"avg");p=e.makeTensorInfo(c.outShape,o.dtype,d.values)}return p}var sP={kernelName:ts,backendName:"cpu",kernelFunc:Jtt};function Qtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n;tt(o,"avgPool3d");let c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.data.get(o.dataId).values,m=Tw(p,o.shape,o.dtype,y.computeStrides(o.shape),c,"avg");return e.makeTensorInfo(m.shape,"float32",m.values)}var iP={kernelName:Oi,backendName:"cpu",kernelFunc:Qtt};function tet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n;tt([o,s],"avgPool3DGrad");let c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,P=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W<c.batchSize;++W)for(let q=0;q<c.inChannels;++q)for(let H=0;H<c.inDepth;++H)for(let K=0;K<c.inHeight;++K)for(let X=0;X<c.inWidth;++X){let Z=H-A,et=K-F,nt=X-D,st=0;for(let at=0;at<I;at+=x){let ot=(Z+at)/p;if(!(ot<0||ot>=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it<N;it+=b){let mt=(et+it)/m;if(!(mt<0||mt>=c.outHeight||Math.floor(mt)!==mt))for(let gt=0;gt<E;gt+=w){let Ct=(nt+gt)/f;if(Ct<0||Ct>=c.outWidth||Math.floor(Ct)!==Ct)continue;let Rt=G.get(W,ot,mt,Ct,q);st+=Rt}}}P.set(st*V,W,H,K,X,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var aP={kernelName:Jl,backendName:"cpu",kernelFunc:tet};function eet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,"float32",A);for(let F=0;F<c.batchSize;++F)for(let P=0;P<c.inChannels;++P)for(let V=0;V<c.inHeight;++V)for(let G=0;G<c.inWidth;++G){let W=V-I,q=G-w,H=0;for(let K=0;K<x;K+=h){let X=(W+K)/p;if(!(X<0||X>=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z<b;Z+=g){let et=(q+Z)/m;if(et<0||et>=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,X,et,P);H+=nt}}N.set(H*E,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var lP={kernelName:Zl,backendName:"cpu",kernelFunc:eet};function ret(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D<c.length;++D)h[D]=d[I++]+(c[D]-p[N++])*f[E++]/Math.sqrt(m[A++]+l),I>=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var uP={kernelName:ys,backendName:"cpu",kernelFunc:ret};function net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Qt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Qt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Bo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var cP={kernelName:Pi,backendName:"cpu",kernelFunc:net};function oet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=bd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var pP={kernelName:Oa,backendName:"cpu",kernelFunc:oet};function set(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var mP={kernelName:Ql,backendName:"cpu",kernelFunc:set};var iet=At(yo,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r<e.clipValueMin?e.clipValueMin:r}),fP={kernelName:yo,backendName:"cpu",kernelFunc:iet};var aet=r=>{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;l<a.length;l++){let c=a[l],p=u[l];n[l]=Math.hypot(c,p)}return e.makeOutput(n,t.shape,"float32")},dP={kernelName:tu,backendName:"cpu",kernelFunc:aet};function va(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.imag,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var hP={kernelName:qp,backendName:"cpu",kernelFunc:va};function Yu(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(h=>h.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Mo({inputs:{input:I},backend:e})),g=u.map(I=>va({inputs:{input:I},backend:e})),x=Yu({inputs:h,backend:e,attrs:{axis:s}}),b=Yu({inputs:g,backend:e,attrs:{axis:s}}),w=Cr({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Qt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=ap(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var gP={kernelName:Mi,backendName:"cpu",kernelFunc:Yu};function _T(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=I.values;for(let Z=0;Z<m.batchSize;++Z){let et=Z*A,nt=Z*V;for(let st=0;st<m.outHeight;++st){let at=nt+st*G,ot=st*m.strideHeight-b;for(let it=0;it<f;++it){let mt=ot+it*h;if(mt<0||mt>=m.inHeight)continue;let gt=it*E[0],Ct=et+mt*D;for(let Rt=0;Rt<m.outWidth;++Rt){let Dt=at+Rt*W,Ht=Rt*m.strideWidth-x;for(let qt=0;qt<d;++qt){let ce=Ht+qt*g;if(ce<0||ce>=m.inWidth)continue;let ge=gt+qt*E[1],re=Ct+ce*F,xe=ge;for(let fe=0;fe<m.inChannels;++fe){let Ae=H[re+fe*P];for(let De=0;De<m.outChannels;++De)X[Dt+De*q]+=Ae*K[xe+De];xe+=m.outChannels}}}}}}return e.makeTensorInfo(I.shape,I.dtype,X)}var xP={kernelName:ns,backendName:"cpu",kernelFunc:_T};function uet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n;tt([o,s],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,x=m.dataFormat==="channelsLast",b=new le(m.filterShape,"float32"),w=m.padInfo.left,I=m.padInfo.top,N=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,A=new le(o.shape,o.dtype,N),D=new le(s.shape,s.dtype,E);for(let F=0;F<h;++F){let P=Math.max(0,Math.ceil((I-F)/f)),V=Math.min(m.outHeight,(m.inHeight+I-F)/f);for(let G=0;G<g;++G){let W=Math.max(0,Math.ceil((w-G)/d)),q=Math.min(m.outWidth,(m.inWidth+w-G)/d);for(let H=0;H<m.inChannels;++H)for(let K=0;K<m.outChannels;++K){let X=0;for(let Z=0;Z<m.batchSize;++Z)for(let et=P;et<V;++et){let nt=F+et*f-I;for(let st=W;st<q;++st){let at=G+st*d-w;x?X+=A.get(Z,nt,at,H)*D.get(Z,et,st,K):X+=A.get(Z,H,nt,at)*D.get(Z,K,et,st)}}b.set(X,F,G,H,K)}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var yP={kernelName:Bp,backendName:"cpu",kernelFunc:uet};function cet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n;tt([o,s],"conv2dBackpropInput");let p=y.computeStrides(s.shape),m=y.computeStrides(o.shape),f=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,f),h=new le(d.inShape,"float32"),g=h.values,x=e.data.get(o.dataId).values,b=e.data.get(s.dataId).values,[w,I,N]=p,{batchSize:E,filterHeight:A,filterWidth:D,inChannels:F,inHeight:P,inWidth:V,outChannels:G,outHeight:W,outWidth:q,strideHeight:H,strideWidth:K}=d;f=d.dataFormat;let X=A-1-d.padInfo.top,Z=D-1-d.padInfo.left,et=f==="channelsLast",nt=h.strides[0],st=et?h.strides[1]:h.strides[2],at=et?h.strides[2]:1,ot=et?1:h.strides[1],it=m[0],mt=et?m[1]:m[2],gt=et?m[2]:1,Ct=et?1:m[1];for(let Rt=0;Rt<E;++Rt)for(let Dt=0;Dt<F;++Dt)for(let Ht=0;Ht<P;++Ht){let qt=Ht-X,ce=Math.max(0,Math.ceil(qt/H)),ge=Math.min(W,(A+qt)/H);for(let re=0;re<V;++re){let xe=re-Z,fe=Math.max(0,Math.ceil(xe/K)),Ae=Math.min(q,(D+xe)/K),De=0;for(let lr=ce;lr<ge;++lr){let eo=lr*H-qt;for(let Vr=fe;Vr<Ae;++Vr){let je=Vr*K-xe,Gr=it*Rt+mt*lr+gt*Vr,Wr=w*(A-1-eo)+I*(D-1-je)+N*Dt;for(let ro=0;ro<G;++ro){let no=x[Gr+Ct*ro],Qr=b[Wr+ro];De+=no*Qr}}}let Ln=nt*Rt+st*Ht+at*re+ot*Dt;g[Ln]=De}}return e.makeTensorInfo(h.shape,h.dtype,h.values)}var bP={kernelName:os,backendName:"cpu",kernelFunc:cet};function pet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;tt([o,s],"conv3d");let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=l,x=g.front,b=g.left,w=g.top,I=new le(l.outShape,o.dtype),N=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,A=I.values,D=y.computeStrides(o.shape),F=y.computeStrides(s.shape);for(let P=0;P<l.batchSize;++P){let V=P*D[0],G=P*I.strides[0];for(let W=0;W<l.outDepth;++W){let q=G+W*I.strides[1],H=W*l.strideDepth-x;for(let K=0;K<c;++K){let X=H+K*f;if(X<0||X>=l.inDepth)continue;let Z=K*F[0],et=V+X*D[1];for(let nt=0;nt<l.outHeight;++nt){let st=q+nt*I.strides[2],at=nt*l.strideHeight-w;for(let ot=0;ot<p;++ot){let it=at+ot*d;if(it<0||it>=l.inHeight)continue;let mt=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct<l.outWidth;++Ct){let Rt=st+Ct*l.outChannels,Dt=Ct*l.strideWidth-b;for(let Ht=0;Ht<m;++Ht){let qt=Dt+Ht*h;if(qt<0||qt>=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,re=ce;for(let xe=0;xe<l.inChannels;++xe){let fe=N[ge+xe];for(let Ae=0;Ae<l.outChannels;++Ae)A[Rt+Ae]+=fe*E[re+Ae];re+=l.outChannels}}}}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}var wP={kernelName:ss,backendName:"cpu",kernelFunc:pet};function met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;tt([o,s],"conv3dBackpropFilterV2");let l=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(o.shape,u,i,1,a),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,x=p.filterWidth,b=new le(p.filterShape,"float32"),w=b.values,[I,N,E,A]=b.strides,D=e.data.get(s.dataId).values,[F,P,V,G]=c,W=e.data.get(o.dataId).values,[q,H,K,X]=l,Z=p.padInfo.front,et=p.padInfo.left,nt=p.padInfo.top;for(let st=0;st<h;++st){let at=Math.max(0,Math.ceil((Z-st)/m)),ot=Math.min(p.outDepth,(p.inDepth+Z-st)/m),it=st*I;for(let mt=0;mt<g;++mt){let gt=Math.max(0,Math.ceil((nt-mt)/f)),Ct=Math.min(p.outHeight,(p.inHeight+nt-mt)/f),Rt=mt*N+it;for(let Dt=0;Dt<x;++Dt){let Ht=Math.max(0,Math.ceil((et-Dt)/d)),qt=Math.min(p.outWidth,(p.inWidth+et-Dt)/d),ce=Dt*E+Rt;for(let ge=0;ge<p.inChannels;++ge){let re=ge*A+ce;for(let xe=0;xe<p.outChannels;++xe){let fe=0;for(let Ae=0;Ae<p.batchSize;++Ae){let De=Ae*q,Ln=Ae*F;for(let lr=at;lr<ot;++lr){let Vr=(st+lr*m-Z)*H+De,je=lr*P+Ln;for(let Gr=gt;Gr<Ct;++Gr){let ro=(mt+Gr*f-nt)*K+Vr,no=Gr*V+je;for(let Qr=Ht;Qr<qt;++Qr){let Wo=(Dt+Qr*d-et)*X+ro,Ei=Qr*G+no;fe+=W[Wo+ge]*D[Ei+xe]}}}}w[re+xe]=fe}}}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var IP={kernelName:Ma,backendName:"cpu",kernelFunc:met};function fet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;tt([o],"conv3dBackpropInputV2");let l=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(u,s.shape,a,1,i),m=new le(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,b=e.data.get(o.dataId).values,[w,I,N,E]=l,A=e.data.get(s.dataId).values,[D,F,P,V]=c,{batchSize:G,filterDepth:W,filterHeight:q,filterWidth:H,inChannels:K,inDepth:X,inHeight:Z,inWidth:et,outChannels:nt,outDepth:st,outHeight:at,outWidth:ot,strideDepth:it,strideHeight:mt,strideWidth:gt}=p,Ct=W-1-p.padInfo.front,Rt=q-1-p.padInfo.top,Dt=H-1-p.padInfo.left;for(let Ht=0;Ht<G;++Ht)for(let qt=0;qt<K;++qt)for(let ce=0;ce<X;++ce){let ge=ce-Ct,re=Math.max(0,Math.ceil(ge/it)),xe=Math.min(st,(W+ge)/it);for(let fe=0;fe<Z;++fe){let Ae=fe-Rt,De=Math.max(0,Math.ceil(Ae/mt)),Ln=Math.min(at,(q+Ae)/mt);for(let lr=0;lr<et;++lr){let eo=lr-Dt,Vr=Math.max(0,Math.ceil(eo/gt)),je=Math.min(ot,(H+eo)/gt),Gr=0;for(let Wr=re;Wr<xe;++Wr){let ro=Wr*it-ge;for(let no=De;no<Ln;++no){let Qr=no*mt-Ae;for(let ka=Vr;ka<je;++ka){let Wo=ka*gt-eo,Ei=w*Ht+I*Wr+N*no+E*ka,Ar=D*(W-1-ro)+F*(q-1-Qr)+P*(H-1-Wo)+V*qt;for(let Ta=0;Ta<nt;++Ta){let qd=b[Ei+Ta],Kd=A[Ar+Ta];Gr+=qd*Kd}}}}f[d*Ht+h*ce+g*fe+x*lr+qt]=Gr}}}return e.makeTensorInfo(m.shape,m.dtype,m.values)}var CP={kernelName:La,backendName:"cpu",kernelFunc:fet};var det=At(is,r=>Math.cos(r)),vP={kernelName:is,backendName:"cpu",kernelFunc:det};var het=At(as,r=>Math.cosh(r)),SP={kernelName:as,backendName:"cpu",kernelFunc:het};function get(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A<d;A++){let D=A*4,F=b[D],P=b[D+1],V=b[D+2],G=b[D+3],W=w[A];if(W>=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K<h;K++){let X=h>1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z<g;Z++)for(let et=0;et<f;et++){let nt=et+Z*E[2]+K*E[1]+A*E[0];x.values[nt]=l}continue}if(u==="bilinear"){let Z=Math.floor(X),et=Math.ceil(X),nt=X-Z;for(let st=0;st<g;st++){let at=g>1?P*(m-1)+st*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt<f;gt++){let Ct=gt+st*E[2]+K*E[1]+A*E[0];x.values[Ct]=l}continue}let ot=Math.floor(at),it=Math.ceil(at),mt=at-ot;for(let gt=0;gt<f;gt++){let Ct=gt+ot*N[2]+Z*N[1]+W*N[0],Rt=I[Ct];Ct=gt+it*N[2]+Z*N[1]+W*N[0];let Dt=I[Ct];Ct=gt+ot*N[2]+et*N[1]+W*N[0];let Ht=I[Ct];Ct=gt+it*N[2]+et*N[1]+W*N[0];let qt=I[Ct],ce=Rt+(Dt-Rt)*mt,ge=Ht+(qt-Ht)*mt;Ct=gt+st*E[2]+K*E[1]+A*E[0],x.values[Ct]=ce+(ge-ce)*nt}}}else for(let Z=0;Z<g;++Z){let et=g>1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;at<f;at++){let ot=at+Z*E[2]+K*E[1]+A*E[0];x.values[ot]=l}continue}let nt=Math.round(et),st=Math.round(X);for(let at=0;at<f;at++){let ot=at+nt*N[2]+st*N[1]+W*N[0],it=at+Z*E[2]+K*E[1]+A*E[0];x.values[it]=I[ot]}}}}return e.makeTensorInfo(x.shape,x.dtype,x.values)}var NP={kernelName:Ba,backendName:"cpu",kernelFunc:get};function xet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;tt(o,"cumprod");let u=S.getAxesPermutation([s],o.shape.length),l=o;u!=null&&(l=Ge({inputs:{x:o},backend:e,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,o.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ur(l.dtype,"int32"),m=y.makeOnesTypedArray(y.sizeFromShape(l.shape),p),f=e.data.get(l.dataId).values,d=l.shape[l.shape.length-1],h=a?(x,b)=>x+d-b-1:(x,b)=>x+b;for(let x=0;x<f.length;x+=d)for(let b=0;b<d;b++){let w=h(x,b);if(b===0)m[w]=i?1:f[w];else{let I=h(x,b-1);m[w]=i?f[I]*m[I]:f[w]*m[I]}}let g=e.makeTensorInfo(l.shape,p,m);if(u!=null){let x=S.getUndoAxesPermutation(u),b=Ge({inputs:{x:g},backend:e,attrs:{perm:x}});return e.disposeIntermediateTensorInfo(g),e.disposeIntermediateTensorInfo(l),b}return g}var kP={kernelName:za,backendName:"cpu",kernelFunc:xet};function yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;tt(o,"cumsum");let u=S.getAxesPermutation([s],o.shape.length),l=o;u!=null&&(l=Ge({inputs:{x:o},backend:e,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,o.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ur(l.dtype,"int32"),m=y.makeZerosTypedArray(y.sizeFromShape(l.shape),p),f=e.data.get(l.dataId).values,d=l.shape[l.shape.length-1],h=a?(x,b)=>x+d-b-1:(x,b)=>x+b;for(let x=0;x<f.length;x+=d)for(let b=0;b<d;b++){let w=h(x,b);if(b===0)m[w]=i?0:f[w];else{let I=h(x,b-1);m[w]=i?f[I]+m[I]:f[w]+m[I]}}let g=e.makeTensorInfo(l.shape,p,m);if(u!=null){let x=S.getUndoAxesPermutation(u),b=Ge({inputs:{x:g},backend:e,attrs:{perm:x}});return e.disposeIntermediateTensorInfo(g),e.disposeIntermediateTensorInfo(l),b}return g}var TP={kernelName:ls,backendName:"cpu",kernelFunc:yet};function bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=bd(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=mw(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var _P={kernelName:eu,backendName:"cpu",kernelFunc:bet};function wet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n;y.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x<a;++x)for(let b=0;b<p;++b){let w=Math.floor(b/s),I=b%s;for(let N=0;N<m;++N){let E=Math.floor(N/s),A=N%s,D=(I*s+A)*f;for(let F=0;F<f;++F){let V=F+D+c*(E+l*(w+u*x));h[g++]=d[V]}}}return e.makeTensorInfo([a,p,m,f],o.dtype,h)}var EP={kernelName:Va,backendName:"cpu",kernelFunc:wet};function ET(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n;tt([o,s],"depthwiseConv2DNative");let c=y.computeStrides(o.shape),p=y.computeStrides(s.shape),m=u;m==null&&(m=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let P=0;P<f.batchSize;++P){let V=P*c[0],G=P*E.strides[0];for(let W=0;W<f.outHeight;++W){let q=G+W*E.strides[1],H=W*f.strideHeight-I;for(let K=0;K<d;++K){let X=H+K*g;if(X<0||X>=f.inHeight)continue;let Z=K*p[0],et=V+X*c[1];for(let nt=0;nt<f.outWidth;++nt){let st=q+nt*E.strides[2],at=nt*f.strideWidth-w;for(let ot=0;ot<h;++ot){let it=at+ot*x;if(it<0||it>=f.inWidth)continue;let mt=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=mt;for(let Dt=0;Dt<f.inChannels;++Dt){let Ht=A[gt+Dt];for(let qt=0;qt<N;++qt)F[Ct+qt]+=Ht*D[Rt+qt];Ct+=N,Rt+=N}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}var AP={kernelName:us,backendName:"cpu",kernelFunc:ET};function Iet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n;tt([o,s],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),{strideHeight:m,strideWidth:f,filterHeight:d,filterWidth:h}=p,g=new le(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=p.outChannels/p.inChannels,I=e.data.get(o.dataId).values,N=new le(o.shape,o.dtype,I),E=e.data.get(s.dataId).values,A=new le(s.shape,s.dtype,E);for(let D=0;D<d;++D){let F=Math.max(0,Math.ceil((b-D)/m)),P=Math.min(p.outHeight,(p.inHeight+b-D)/m);for(let V=0;V<h;++V){let G=Math.max(0,Math.ceil((x-V)/f)),W=Math.min(p.outWidth,(p.inWidth+x-V)/f);for(let q=0;q<p.outChannels;++q){let H=Math.trunc(q/w),K=q%w,X=0;for(let Z=0;Z<p.batchSize;++Z)for(let et=F;et<P;++et){let nt=D+et*m-b;for(let st=G;st<W;++st){let at=V+st*f-x;X+=N.get(Z,nt,at,H)*A.get(Z,et,st,q)}}g.set(X,D,V,H,K)}}}return e.makeTensorInfo(g.shape,g.dtype,g.values)}var DP={kernelName:Vp,backendName:"cpu",kernelFunc:Iet};function Cet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n;tt([o,s],"depthwiseConv2DNativeBackpropInput");let p=y.computeStrides(o.shape),m=y.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),d=new le(f.inShape,"float32"),h=d.values,[g,x,b]=d.strides,w=e.data.get(o.dataId).values,[I,N,E]=p,A=e.data.get(s.dataId).values,[D,F,P]=m,{batchSize:V,filterHeight:G,filterWidth:W,inChannels:q,inHeight:H,inWidth:K,outChannels:X,outHeight:Z,outWidth:et,strideHeight:nt,strideWidth:st}=f,at=G-1-f.padInfo.top,ot=W-1-f.padInfo.left,it=X/q;for(let mt=0;mt<V;++mt)for(let gt=0;gt<q;++gt)for(let Ct=0;Ct<H;++Ct){let Rt=Ct-at,Dt=Math.max(0,Math.ceil(Rt/nt)),Ht=Math.min(Z,(G+Rt)/nt);for(let qt=0;qt<K;++qt){let ce=qt-ot,ge=Math.max(0,Math.ceil(ce/st)),re=Math.min(et,(W+ce)/st),xe=0;for(let fe=Dt;fe<Ht;++fe){let Ae=fe*nt-Rt;for(let De=ge;De<re;++De){let Ln=De*st-ce,lr=I*mt+N*fe+E*De,eo=D*(G-1-Ae)+F*(W-1-Ln)+P*gt;for(let Vr=0;Vr<it;++Vr){let je=gt*it+Vr,Gr=w[lr+je],Wr=A[eo+Vr];xe+=Gr*Wr}}}h[g*mt+x*Ct+b*qt+gt]=xe}}return e.makeTensorInfo(d.shape,d.dtype,d.values)}var $P={kernelName:Gp,backendName:"cpu",kernelFunc:Cet};function vet(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.data.get(n.dataId).values,i=wt([o,o],n.dtype),a=i.values;for(let l=0;l<s.length;l++)a[l*o+l]=s[l];let u=[...n.shape,...n.shape];return e.makeTensorInfo(u,i.dtype,i.values)}var RP={kernelName:ru,backendName:"cpu",kernelFunc:vet};var FP={kernelName:cs,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H<f;++H)for(let K=0;K<x;++K){let X=K*I-w.top;for(let Z=0;Z<b;++Z){let et=Z*N-w.left;for(let nt=0;nt<g;++nt){let st=Number.MIN_SAFE_INTEGER;for(let ot=0;ot<E;++ot){let it=X+ot*D;if(it>=0&&it<d)for(let mt=0;mt<A;++mt){let gt=et+mt*F;if(gt>=0&&gt<h){let Ct=y.locToIndex([H,it,gt,nt],c,y.computeStrides(n.shape)),Rt=y.locToIndex([ot,mt,nt],m,y.computeStrides(o.shape)),Dt=l[Ct]+p[Rt];Dt>st&&(st=Dt)}}}let at=y.locToIndex([H,K,Z,nt],G,y.computeStrides(P));W[at]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var OP={kernelName:ou,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${ou}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W<m;++W)for(let q=0;q<g;++q){let H=q*w-b.top;for(let K=0;K<x;++K){let X=K*I-b.left;for(let Z=0;Z<h;++Z){let et=Number.MIN_SAFE_INTEGER,nt=0,st=0;for(let at=0;at<N;++at){let ot=H+at*A;if(ot>=0&&ot<f)for(let it=0;it<E;++it){let mt=X+it*D;if(mt>=0&&mt<d){let gt=c[W][ot][mt][Z]+p[at][it][Z];gt>et&&(et=gt,nt=at,st=it)}}}V[nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var PP={kernelName:nu,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${nu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W<m;++W)for(let q=0;q<g;++q){let H=q*w-b.top;for(let K=0;K<x;++K){let X=K*I-b.left;for(let Z=0;Z<h;++Z){let et=Number.MIN_SAFE_INTEGER,nt=H<0?0:H,st=X<0?0:X;for(let at=0;at<N;++at){let ot=H+at*A;if(ot>=0&&ot<f)for(let it=0;it<E;++it){let mt=X+it*D;if(mt>=0&&mt<d){let gt=c[W][ot][mt][Z]+p[at][it][Z];gt>et&&(et=gt,nt=ot,st=mt)}}}V[W][nt][st][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w<m*f;++w){let I=[0,0,0,255*l];for(let E=0;E<d;E++){let A=h[w*d+E];if(o.dtype==="float32"){if(A<0||A>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype==="int32"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var MP={kernelName:Zg,backendName:"cpu",kernelFunc:Net};function zl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Lo({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=xd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I<b.length;++I){let N=I*x,E=0;for(let A=0;A<x;++A)E+=w[N+A];b[I]=E}if(i){let I=S.expandShapeToKeepDim(g.shape,l),N=g;g=Qt({inputs:{x:g},backend:e,attrs:{shape:I}}),e.disposeIntermediateTensorInfo(N)}return e.disposeIntermediateTensorInfo(a),c!=null&&e.disposeIntermediateTensorInfo(m),g}var LP={kernelName:ri,backendName:"cpu",kernelFunc:zl};function ket(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(x)?w=s[g]:(w=Ge({inputs:{x:s[g]},backend:e,attrs:{perm:x}}),d.push(w));let I=w.shape.slice();for(let N=0;N<b.length;++N)I.splice(b[N],0,1);y.arraysEqual(w.shape,I)||(w=Qt({inputs:{x:w},backend:e,attrs:{shape:I}}),d.push(w)),m===null?m=w:(m=lp({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=zl({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var zP={kernelName:Wp,backendName:"cpu",kernelFunc:ket};function Tet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u<i.length;++u){let l=i[u];l>=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var BP={kernelName:Ga,backendName:"cpu",kernelFunc:Tet};var _et=S.ERF_P,Eet=S.ERF_A1,Aet=S.ERF_A2,Det=S.ERF_A3,$et=S.ERF_A4,Ret=S.ERF_A5,Fet=At(fs,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+_et*e);return t*(1-((((Ret*n+$et)*n+Det)*n+Aet)*n+Eet)*n*Math.exp(-e*e))}),VP={kernelName:fs,backendName:"cpu",kernelFunc:Fet};function Sd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Qt({inputs:{x:o},backend:e,attrs:{shape:a}})}var GP={kernelName:Li,backendName:"cpu",kernelFunc:Sd};var Oet=Jt((r,t)=>r/t),eg=oe(ps,Oet),rg={kernelName:ps,backendName:"cpu",kernelFunc:eg};function _w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g<o;g++){let x=Bo({inputs:{x:a},backend:e,attrs:{begin:[g,0],size:[1,s]}}),b=Bo({inputs:{x:u},backend:e,attrs:{begin:[g,0],size:[1,s]}}),w=Cr({inputs:{real:x,imag:b},backend:e}),{real:I,imag:N}=Pet(w,t,e),E=S.mergeRealAndImagArrays(I,N);for(let A=0;A<s;A++){let D=S.getComplexWithIndex(E,A);p[g*s+A]=D.real,m[g*s+A]=D.imag}e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(w)}let f=e.makeTensorInfo(l,"float32",p),d=e.makeTensorInfo(l,"float32",m),h=Cr({inputs:{real:f,imag:d},backend:e});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),h}function Pet(r,t,e){let n=y.sizeFromShape(r.shape),o=e.data.get(r.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,i=e.data.get(o.complexTensorInfos.imag.dataId).values;if(Met(n)){let a=AT(s,i,n,t,e),u=[r.shape[0],r.shape[1]];if(t){let l=e.makeTensorInfo(u,"float32",a.real),c=e.makeTensorInfo(u,"float32",a.imag),p=e.makeTensorInfo([],"float32",y.createScalarValue(n,"float32")),m=Zr({inputs:{x:p},backend:e}),f=rg.kernelFunc({inputs:{a:l,b:p},backend:e}),d=rg.kernelFunc({inputs:{a:c,b:m},backend:e}),h=e.data.get(f.dataId).values,g=e.data.get(d.dataId).values;return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),{real:h,imag:g}}return a}else{let a=S.mergeRealAndImagArrays(s,i),u=Let(a,n,t);return S.splitRealAndImagArrays(u)}}function Met(r){return(r&r-1)===0}function AT(r,t,e,n,o){if(e===1)return{real:r,imag:t};let s=S.mergeRealAndImagArrays(r,t),i=e/2,a=S.complexWithEvenIndex(s),u=a.real,l=a.imag,c=[u.length],p=o.makeTensorInfo(c,"float32",u),m=o.makeTensorInfo(c,"float32",l),f=Cr({inputs:{real:p,imag:m},backend:o}),d=S.complexWithOddIndex(s),h=d.real,g=d.imag,x=[h.length],b=o.makeTensorInfo(x,"float32",h),w=o.makeTensorInfo(x,"float32",g),I=Cr({inputs:{real:b,imag:w},backend:o}),N=AT(u,l,i,n,o),E=N.real,A=N.imag,D=[E.length],F=o.makeTensorInfo(D,"float32",E),P=o.makeTensorInfo(D,"float32",A),V=Cr({inputs:{real:F,imag:P},backend:o}),G=AT(h,g,i,n,o),W=G.real,q=G.imag,H=[W.length],K=o.makeTensorInfo(H,"float32",W),X=o.makeTensorInfo(H,"float32",q),Z=Cr({inputs:{real:K,imag:X},backend:o}),et=S.exponents(e,n),nt=[et.real.length],st=o.makeTensorInfo(nt,"float32",et.real),at=o.makeTensorInfo(nt,"float32",et.imag),ot=Cr({inputs:{real:st,imag:at},backend:o}),it=lp({inputs:{a:ot,b:Z},backend:o}),mt=Ca({inputs:{a:V,b:it},backend:o}),gt=Qh({inputs:{a:V,b:it},backend:o}),Ct=Mo({inputs:{input:mt},backend:o}),Rt=Mo({inputs:{input:gt},backend:o}),Dt=va({inputs:{input:mt},backend:o}),Ht=va({inputs:{input:gt},backend:o}),qt=Yu({inputs:[Ct,Rt],backend:o,attrs:{axis:0}}),ce=Yu({inputs:[Dt,Ht],backend:o,attrs:{axis:0}}),ge=o.data.get(qt.dataId).values,re=o.data.get(ce.dataId).values;return o.disposeIntermediateTensorInfo(p),o.disposeIntermediateTensorInfo(m),o.disposeIntermediateTensorInfo(f),o.disposeIntermediateTensorInfo(b),o.disposeIntermediateTensorInfo(w),o.disposeIntermediateTensorInfo(I),o.disposeIntermediateTensorInfo(F),o.disposeIntermediateTensorInfo(P),o.disposeIntermediateTensorInfo(V),o.disposeIntermediateTensorInfo(K),o.disposeIntermediateTensorInfo(X),o.disposeIntermediateTensorInfo(Z),o.disposeIntermediateTensorInfo(st),o.disposeIntermediateTensorInfo(at),o.disposeIntermediateTensorInfo(ot),o.disposeIntermediateTensorInfo(it),o.disposeIntermediateTensorInfo(mt),o.disposeIntermediateTensorInfo(gt),o.disposeIntermediateTensorInfo(Ct),o.disposeIntermediateTensorInfo(Dt),o.disposeIntermediateTensorInfo(Rt),o.disposeIntermediateTensorInfo(Ht),o.disposeIntermediateTensorInfo(qt),o.disposeIntermediateTensorInfo(ce),{real:ge,imag:re}}function Let(r,t,e){let n=new Float32Array(t*2);for(let o=0;o<t;o++){let s=0,i=0;for(let a=0;a<t;a++){let u=S.exponent(o*a,t,e),l=S.getComplexWithIndex(r,a);s+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}e&&(s/=t,i/=t),S.assignToTypedArray(n,s,i,o)}return n}function zet(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Qt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=_w(a,!1,e),l=Qt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var WP={kernelName:Up,backendName:"cpu",kernelFunc:zet};function ng(r){let{backend:t,attrs:e}=r,{shape:n,value:o,dtype:s}=e,i=s||y.inferDtype(o),a=y.getArrayFromDType(i,y.sizeFromShape(n));return Bet(a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t{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;tt(i,"LRNGrad");let p=y.sizeFromShape(i.shape),m=i.shape[3],f=e.data.get(i.dataId).values,d=e.data.get(o.dataId).values,h=e.data.get(s.dataId).values,g=new Float32Array(p),x=p;for(let b=0;b<x;b++){let w=b%m,I=b-w+Math.max(0,w-a),N=b-w+Math.min(m,w+a+1),E=0;for(let A=I;A<N;A++)E+=Math.pow(d[A],2);E=l*E+u;for(let A=I;A<N;A++){let D=-2*l*c*d[A]*h[b]/E;b===A&&(D+=Math.pow(E,-c)),D*=f[b],g[A]+=D}}return e.makeTensorInfo(i.shape,o.dtype,g)}var iM={kernelName:Qa,backendName:"cpu",kernelFunc:nrt};function DT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=e,u=o.shape,l=u.length,c=y.parseAxisParam(s,u),p=c,m=S.getAxesPermutation(p,l),f=a.data.get(o.dataId).values;if(m!=null){let I=new Array(l);for(let N=0;N<I.length;N++)I[N]=u[m[N]];f=wd(f,u,o.dtype,m,I),p=S.getInnerMostAxes(p.length,l),u=I}tt(o,"max"),S.assertAxesAreInnerMostDims("max",p,l);let[d,h]=S.computeOutAndReduceShapes(u,p),g=y.sizeFromShape(h),x=gw(f,g,d,o.dtype),b=a.write(x,d,o.dtype),w=d;return i&&(w=S.expandShapeToKeepDim(d,c)),{dataId:b,shape:w,dtype:o.dtype}}var aM={kernelName:Ts,backendName:"cpu",kernelFunc:DT};function ort(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Zr({inputs:{x:o},backend:e});else{let m=e.data.get(o.dataId).values,f=y.computeStrides(o.shape),d=vd(m,o.shape,o.dtype,f,c,"max");p=e.makeTensorInfo(c.outShape,o.dtype,d.values)}return p}var lM={kernelName:Es,backendName:"cpu",kernelFunc:ort};function srt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n;tt(o,"maxPool3d");let c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.data.get(o.dataId).values,m=Tw(p,o.shape,o.dtype,y.computeStrides(o.shape),c,"max");return e.makeTensorInfo(m.shape,"float32",m.values)}var uM={kernelName:Bi,backendName:"cpu",kernelFunc:srt};function irt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n;tt([o,s],"maxPool3DGrad");let c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.bufferSync(s),m=oP(p,c),f=c.strideDepth,d=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,w=c.effectiveFilterDepth,I=c.effectiveFilterHeight,N=c.effectiveFilterWidth,E=w-1-c.padInfo.front,A=N-1-c.padInfo.left,D=I-1-c.padInfo.top,F=wt(s.shape,"float32"),P=e.bufferSync(o);for(let V=0;V<c.batchSize;++V)for(let G=0;G<c.inChannels;++G)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let H=0;H<c.inWidth;++H){let K=W-E,X=q-D,Z=H-A,et=0;for(let nt=0;nt<w;nt+=g){let st=(K+nt)/f;if(!(st<0||st>=c.outDepth||Math.floor(st)!==st))for(let at=0;at<I;at+=x){let ot=(X+at)/d;if(!(ot<0||ot>=c.outHeight||Math.floor(ot)!==ot))for(let it=0;it<N;it+=b){let mt=(Z+it)/h;if(mt<0||mt>=c.outWidth||Math.floor(mt)!==mt)continue;let gt=w*I*N-1-m.get(V,st,ot,mt,G),Ct=nt*I*N+at*N+it,Rt=gt===Ct?1:0;if(Rt===0)continue;let Dt=P.get(V,st,ot,mt,G);et+=Dt*Rt}}}F.set(et,V,W,q,H,G)}return e.makeTensorInfo(F.shape,F.dtype,F.values)}var cM={kernelName:au,backendName:"cpu",kernelFunc:irt};function art(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;tt([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=e.data.get(a.dataId).values,d=wt(m.outShape,a.dtype,kw(f,a.shape,a.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,w=m.effectiveFilterHeight,I=m.effectiveFilterWidth,N=I-1-m.padInfo.left,E=w-1-m.padInfo.top,A=wt(a.shape,"float32"),D=e.data.get(o.dataId).values,F=wt(o.shape,"float32",D);for(let P=0;P<m.batchSize;++P)for(let V=0;V<m.inChannels;++V)for(let G=0;G<m.inHeight;++G)for(let W=0;W<m.inWidth;++W){let q=G-E,H=W-N,K=0;for(let X=0;X<w;X+=x){let Z=(q+X)/h;if(!(Z<0||Z>=m.outHeight||Math.floor(Z)!==Z))for(let et=0;et<I;et+=b){let nt=(H+et)/g;if(nt<0||nt>=m.outWidth||Math.floor(nt)!==nt)continue;let st=w*I-1-d.get(P,Z,nt,V),at=X*I+et,ot=st===at?1:0;if(ot===0)continue;let it=F.get(P,Z,nt,V);K+=it*ot}}A.set(K,P,G,W,V)}return e.makeTensorInfo(A.shape,A.dtype,A.values)}var pM={kernelName:iu,backendName:"cpu",kernelFunc:art};function mM(r,t,e,n,o){let s=y.computeStrides(t),i=vd(r,t,e,s,o,"max"),a=kw(r,t,e,o,!0,n);return[i.values,a.values]}var fM={kernelName:lu,backendName:"cpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;tt(n,"MaxPoolWithArgmax");let l=u.data.get(n.dataId).values,c=S.computePool2DInfo(n.shape,o,s,[1,1],i),[p,m]=mM(l,n.shape,n.dtype,a,c),f=u.write(p,c.outShape,n.dtype),d=u.write(m,c.outShape,n.dtype);return[{dataId:f,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t c=e.data.get(o.dataId).values,p=e.data.get(s.dataId).values,m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=yrt(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var vM={kernelName:ol,backendName:"cpu",kernelFunc:brt};function wrt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n;tt(o,"oneHot");let l=y.sizeFromShape(o.shape),c=new Float32Array(l*i);c.fill(u);let p=e.data.get(o.dataId).values;for(let m=0;m<l;++m)p[m]>=0&&p[m]<i&&(c[m*i+p[m]]=a);return e.makeTensorInfo([...o.shape,i],s,c)}var SM={kernelName:Ps,backendName:"cpu",kernelFunc:wrt};function og(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(n.dtype==="complex64"){let o=Mo({inputs:{input:n},backend:e}),s=og({inputs:{x:o},backend:e}),i=va({inputs:{input:n},backend:e}),a=og({inputs:{x:i},backend:e}),u=Cr({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return ng({backend:e,attrs:{shape:n.shape,value:0,dtype:n.dtype}})}var NM={kernelName:Yi,backendName:"cpu",kernelFunc:og};function kM(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(n.dtype==="complex64"){let o=Mo({inputs:{input:n},backend:e}),s=kM({inputs:{x:o},backend:e}),i=va({inputs:{input:n},backend:e}),a=og({inputs:{x:i},backend:e}),u=Cr({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return ng({backend:e,attrs:{shape:n.shape,value:1,dtype:n.dtype}})}var TM={kernelName:Gi,backendName:"cpu",kernelFunc:kM};function RT(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return Sd({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=Sd({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Yu({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var _M={kernelName:Wi,backendName:"cpu",kernelFunc:RT};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;tt(o,"pad");let a=s.map((b,w)=>b[0]+o.shape[w]+b[1]),u=s.map(b=>b[0]),l=e.data.get(o.dataId).values,c=y.sizeFromShape(o.shape),p=o.shape.length,m=y.computeStrides(o.shape),f=y.sizeFromShape(a),d=a.length,h=y.computeStrides(a),g=y.getTypedArrayFromDType(o.dtype,f);i!==0&&g.fill(i);for(let b=0;b<c;b++){let I=y.indexToLoc(b,p,m).map((E,A)=>E+u[A]),N=y.locToIndex(I,d,h);g[N]=l[b]}return{dataId:e.write(g,a,o.dtype),shape:a,dtype:o.dtype}}var Ew={kernelName:Ms,backendName:"cpu",kernelFunc:Irt};var Crt=Jt((r,t)=>Math.pow(r,t)),vrt=oe(Ls,Crt),EM={kernelName:Ls,backendName:"cpu",kernelFunc:vrt};function Srt(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.data.get(x.dataId).values),l=o.map(x=>x.shape),c=e.data.get(s.dataId).values,p=e.data.get(i.dataId).values,[m,f,d]=xw(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var AM={kernelName:Kp,backendName:"cpu",kernelFunc:Srt};function Nrt(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=yw(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var DM={kernelName:jp,backendName:"cpu",kernelFunc:Nrt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.data.get(o.dataId).values,c=e.data.get(s.dataId).values,p=e.data.get(i.dataId).values,m=a.map(g=>e.data.get(g.dataId).values),f=a.map(g=>g.shape),[d,h]=bw(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var $M={kernelName:Xp,backendName:"cpu",kernelFunc:krt};function Trt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,dtype:s,step:i}=e,a=up(n,o,i,s);return t.makeTensorInfo([a.length],s,a)}var RM={kernelName:uu,backendName:"cpu",kernelFunc:Trt};var _rt=At(Vs,r=>1/r),FM={kernelName:Vs,backendName:"cpu",kernelFunc:_rt};function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n;tt(o,"resizeBilinear");let u=y.computeStrides(o.shape),[l,c]=a,[p,m,f,d]=o.shape,h=e.data.get(o.dataId).values,g=new Float32Array(y.sizeFromShape([p,l,c,d])),x=[s&&l>1?m-1:m,s&&c>1?f-1:f],b=[s&&l>1?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=0,n=r.length,o=0;for(;e<n;)o=Math.floor((e+n)/2),r[o]<t?e=o+1:n=o;return n}function Mrt(r,t){let e=0,n=r.length,o=0;for(;e<n;)o=Math.floor((e+n)/2),r[o]<=t?e=o+1:n=o;return n}function WM(r,t,e,n,o,s){let i=y.getArrayFromDType("int32",e*o);for(let a=0;a<e;++a){let u=r.slice(a*n,(a+1)*n),l=a*o;for(let c=0;c<o;++c)i[l+c]=s==="left"?Prt(u,t[c+l]):Mrt(u,t[c+l])}return i}function Lrt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=WM(a,u,o.shape[0],o.shape[1],s.shape[1],i);return e.makeTensorInfo(s.shape,"int32",l)}var UM={kernelName:ul,backendName:"cpu",kernelFunc:Lrt};function zrt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t;tt([n,o,s],"select");let i=n.shape.length,a=e.data.get(n.dataId).values,u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=ur(o.dtype,s.dtype),p=y.makeZerosTypedArray(y.sizeFromShape(o.shape),c),m=0,f=i===0||i>1||o.shape.length===1?1:y.sizeFromShape(o.shape.slice(1));for(let d=0;d<a.length;d++)for(let h=0;h<f;h++)a[d]===1?p[m++]=u[d]:p[m++]=l[d];return e.makeTensorInfo(o.shape,c,p)}var HM={kernelName:Hi,backendName:"cpu",kernelFunc:zrt};var Brt=S.SELU_SCALEALPHA,Vrt=S.SELU_SCALE,Grt=At(Xs,r=>r>=0?Vrt*r:Brt*(Math.exp(r)-1)),qM={kernelName:Xs,backendName:"cpu",kernelFunc:Grt};var Wrt=At(Js,r=>r<0?-1:r>0?1:0),KM={kernelName:Js,backendName:"cpu",kernelFunc:Wrt};var Urt=At(Ys,r=>Math.sin(r)),jM={kernelName:Ys,backendName:"cpu",kernelFunc:Urt};var Hrt=At(Zs,r=>Math.sinh(r)),XM={kernelName:Zs,backendName:"cpu",kernelFunc:Hrt};var qrt=11920928955078125e-23,YM=Math.log(qrt)+2,Krt=At(ti,r=>{let t=r>-YM,e=r<YM,n=Math.exp(r),o;return e?o=n:t?o=r:o=Math.log(1+n),o}),ZM={kernelName:ti,backendName:"cpu",kernelFunc:Krt};function jrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;tt([o],"spaceToBatchND");let a=y.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let E=1+s.length;E<o.shape.length;++E)u.push([0,0]);let l=Ew.kernelFunc({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,s,a,!1),p=S.getPermuted(c.length,s.length,!1),m=S.getReshapedPermuted(l.shape,s,a,!1),h=Qt({inputs:{x:l},backend:e,attrs:{shape:c}}),b=Ge({inputs:{x:h},backend:e,attrs:{perm:p}}),N=Qt({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(b),N}var JM={kernelName:Ki,backendName:"cpu",kernelFunc:jrt};function Xrt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
        ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
        ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
        ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let a=e.data.get(n.dataId).values,u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=e.data.get(i.dataId).values[0],[p,m,f,d,h]=ww(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var QM={kernelName:cu,backendName:"cpu",kernelFunc:Xrt};function Yrt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.data.get(o.dataId).values),a=e.data.get(n.dataId).values,u=Array.from(e.data.get(s.dataId).values),[l,c,p]=Iw(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var tL={kernelName:cl,backendName:"cpu",kernelFunc:Yrt};function Zrt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
          ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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         ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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`))}function BT(r){return Bl(r,()=>r.createProgram(),"Unable to create WebGLProgram.")}function VT(r,t){if(ht(r,()=>r.linkProgram(t)),!L().get("ENGINE_COMPILE_ONLY")&&r.getProgramParameter(t,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function lg(r,t){if(ht(r,()=>r.validateProgram(t)),r.getProgramParameter(t,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function GT(r,t){let e=Bl(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),ht(r,()=>r.bufferData(r.ARRAY_BUFFER,t,r.STATIC_DRAW)),e}function WT(r,t){let e=Bl(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ht(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,e)),ht(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,t,r.STATIC_DRAW)),e}function _nt(){return L().getNumber("WEBGL_VERSION")===2?1:4}function UT(r){return Bl(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function HT(r,t){let e=L().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||t<=0){let n=`[${r}x${t}]`;throw new Error("Requested texture size "+n+" is invalid.")}if(r>e||t>e){let n=`[${r}x${t}]`,o=`[${e}x${e}]`;throw new Error("Requested texture size "+n+" greater than WebGL maximum on this browser / GPU "+o+".")}}function qT(r){return Bl(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Ow(r,t,e,n,o,s,i){let a=r.getAttribLocation(t,e);return a===-1?!1:(ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,n)),ht(r,()=>r.vertexAttribPointer(a,o,r.FLOAT,!1,s,i)),ht(r,()=>r.enableVertexAttribArray(a)),!0)}function SL(r,t,e){kL(r,e),ht(r,()=>r.activeTexture(r.TEXTURE0+e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,t))}function Ent(r,t){kL(r,t),ht(r,()=>r.activeTexture(r.TEXTURE0+t)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function KT(r,t,e){return Bl(r,()=>r.getUniformLocation(t,e),'uniform "'+e+'" not present in program.')}function jT(r,t,e){return r.getUniformLocation(t,e)}function XT(r,t,e,n){ht(r,()=>SL(r,t,n)),ht(r,()=>r.uniform1i(e,n))}function Ant(r){ht(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,null)),ht(r,()=>r.viewport(0,0,r.canvas.width,r.canvas.height)),ht(r,()=>r.scissor(0,0,r.canvas.width,r.canvas.height))}function ug(r,t,e){ht(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,e)),ht(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t,0))}function Pw(r,t){ht(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,t)),ht(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,null,0))}function kd(r){let t=r.checkFramebufferStatus(r.FRAMEBUFFER);if(t!==r.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+NL(r,t))}function NL(r,t){switch(t){case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case r.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function Bl(r,t,e){let n=ht(r,()=>t());if(n==null)throw new Error(e);return n}function kL(r,t){let e=r.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,n=t+r.TEXTURE0;if(n<r.TEXTURE0||n>e){let o=`[gl.TEXTURE0, gl.TEXTURE${e}]`;throw new Error(`textureUnit must be in ${o}.`)}}function Vl(r,t=2){return y.sizeFromShape(r.slice(0,r.length-t))}function Gl(r){if(r.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[r.length>1?r[r.length-2]:1,r[r.length-1]]}function Td(r){let t=[1,1,1];return r.length===0||r.length===1&&r[0]===1||(t=[Vl(r),...Gl(r)]),t}function YT(r,t=!1){let e=L().getNumber("WEBGL_MAX_TEXTURE_SIZE"),n=L().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");n===1/0&&L().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(n=e/2),t&&(e=e*2,n=n*2,r=r.map((a,u)=>u>=r.length-2?y.nearestLargerEven(r[u]):r[u]),r.length===1&&(r=[2,r[0]])),r.length!==2&&(r=y.squeezeShape(r).newShape);let o=y.sizeFromShape(r),s=null;r.length<=1&&o<=e?s=[1,o]:r.length===2&&r[0]<=e&&r[1]<=e?s=r:r.length===3&&r[0]*r[1]<=e&&r[2]<=e?s=[r[0]*r[1],r[2]]:r.length===3&&r[0]<=e&&r[1]*r[2]<=e?s=[r[0],r[1]*r[2]]:r.length===4&&r[0]*r[1]*r[2]<=e&&r[3]<=e?s=[r[0]*r[1]*r[2],r[3]]:r.length===4&&r[0]<=e&&r[1]*r[2]*r[3]<=e&&(s=[r[0],r[1]*r[2]*r[3]]);let i=s!=null&&Math.max(...s)>n&&Math.min(...s)<=(t?2:1)&&Math.min(...s)>0;if(s==null||i)if(t){let a=Vl(r),u=2,l=2;r.length&&([u,l]=Gl(r)),o=a*(u/2)*(l/2),s=y.sizeToSquarishShape(o).map(c=>c*2)}else s=y.sizeToSquarishShape(o);return s}function Dw(r){return r%2===0}function Ju(r,t){if(r=r.slice(-2),t=t.slice(-2),y.arraysEqual(r,t)||!r.length||!t.length||r[0]===0||r[1]===0||t[0]===0||t[1]===0)return!0;if(r.length!==t.length){let e=r[r.length-1],n=t[t.length-1];if(e===n||Dw(e)&&Dw(n)&&(r[0]===1||t[0]===1))return!0}return r[1]===t[1]&&Dw(r[0])&&Dw(t[0])}var $w,Rw;function ZT(r){if($w==null){let t=Yn(r);$w=t.getParameter(t.MAX_TEXTURE_SIZE)}return $w}function Dnt(){$w=null}function $nt(){Rw=null}function JT(r){if(Rw==null){let t=Yn(r);Rw=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Rw)}function QT(r){if(r===0)return 0;let t,e=Yn(r);return Zn(e,"EXT_disjoint_timer_query_webgl2")&&r===2?t=2:Zn(e,"EXT_disjoint_timer_query")?t=1:t=0,t}function Zn(r,t){return r.getExtension(t)!=null}function Mw(r){try{if(Yn(r)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function t1(r){if(r===0)return!1;let t=Yn(r);if(r===1){if(!Zn(t,"OES_texture_float"))return!1}else if(!Zn(t,"EXT_color_buffer_float"))return!1;return PT(t)}function e1(r){if(r===0)return!1;let t=Yn(r);if(r===1){if(!Zn(t,"OES_texture_float")||!Zn(t,"WEBGL_color_buffer_float"))return!1}else{if(Zn(t,"EXT_color_buffer_float"))return PT(t);let n="EXT_color_buffer_half_float";if(Zn(t,n)){let o=t.getExtension(n);return Rnt(t,o)}return!1}return PT(t)}function PT(r){let t=ag(r),e=r.createTexture();r.bindTexture(r.TEXTURE_2D,e);let n=1,o=1;r.texImage2D(r.TEXTURE_2D,0,t.internalFormatFloat,n,o,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,s),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0);let i=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(e),r.deleteFramebuffer(s),i}function Rnt(r,t){let e=ag(r,t),n=r.createTexture();r.bindTexture(r.TEXTURE_2D,n);let o=1,s=1;r.texImage2D(r.TEXTURE_2D,0,e.internalFormatHalfFloat,o,s,0,e.textureFormatFloat,e.textureTypeHalfFloat,null);let i=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,i),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,n,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(n),r.deleteFramebuffer(i),a}function r1(r){return r!==2?!1:Yn(r).fenceSync!=null}function Ni(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Et=L();Et.registerFlag("HAS_WEBGL",()=>Et.getNumber("WEBGL_VERSION")>0);Et.registerFlag("WEBGL_VERSION",()=>Mw(2)?2:Mw(1)?1:0);Et.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Et.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Et.get("WEBGL_VERSION")===2);Et.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Et.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Et.registerFlag("WEBGL_PACK",()=>Et.getBool("HAS_WEBGL"));Et.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_CLIP",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_REDUCE",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_LAZILY_UNPACK",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_CONV_IM2COL",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>ZT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>JT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Et.getNumber("WEBGL_VERSION");return r===0?0:QT(r)});Et.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Et.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Cu.isMobile());Et.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>t1(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Et.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Et.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Et.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>e1(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_FENCE_API_ENABLED",()=>r1(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Et.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Et.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Et.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Cu.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Et.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Et.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Et.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Et.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Et.registerFlag("WEBGL_EXP_CONV",()=>!1);Et.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Et.getBool("IS_TEST"));Et.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Et.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Et.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Et.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function We(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",t="in",e="out",n="in",o="texture",s="outputColor",i="out vec4 outputColor;",a=L().getBool("WEBGL2_ISNAN_CUSTOM")?`
      bool isnan_custom(float val) {
        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }
 
      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)
    `:"",u="",l=`
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }
 
      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `):(r="",t="attribute",e="varying",n="varying",o="texture2D",s="gl_FragColor",i="",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));
      }
    `,u=`
      uniform float INFINITY;
 
      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,l=`
      int round(float value) {
        return int(floor(value + 0.5));
      }
 
      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function ki(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function yp(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Fnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function TL(r,t,e="index"){let n=r.map((s,i)=>i),o=Fnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function Ed(r){let t=y.computeStrides(r).map(e=>e.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function Ad(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var Lw=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;
 
  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }
 
    highp float av = abs(v);
 
    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }
 
    highp vec4 c = vec4(0,0,0,0);
 
    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;
 
    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);
 
    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;
 
    c[3] += 128.0 * step(0.0, -v);
 
    return c / 255.0;
  }
`;var{getBroadcastDims:_L}=S;function EL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=zw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
`),s=r.map(f=>Ont(f,t,e.packedInputs,e.enableShapeUniforms)).join(`
`),i=t.texShape,a=We(),u=Lnt(a),l,c,p=Vnt(a);return t.isPacked?(l=Pnt(t.logicalShape,i,e.enableShapeUniforms),c=Bnt(a)):(l=Mnt(t.logicalShape,i,e.enableShapeUniforms),c=znt(a)),e.packedInputs&&(p+=Hnt),[p,u,c,o,l,s,e.userCode].join(`
`)}function $d(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return not(r,t);case 1:return sot(r,t);case 2:return aot(r,t);case 3:return uot(r,t);case 4:return pot(r,t);case 5:return mot(r);case 6:return fot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function AL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return rot(r);case 1:return oot(r,t);case 2:return iot(r,t);case 3:return lot(r,t);default:return cot(r,t)}}function Ont(r,t,e=!1,n){let o="";e?o+=AL(r,n):o+=$d(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=dot(r,t):o+=hot(r,t)),o}function Pnt(r,t,e){switch(r.length){case 0:return DL();case 1:return qnt(r,t,e);case 2:return tot(r,t,e);case 3:return jnt(r,t,e);default:return Ynt(r,t,e)}}function Mnt(r,t,e){switch(r.length){case 0:return DL();case 1:return Knt(r,t,e);case 2:return eot(r,t,e);case 3:return Xnt(r,t,e);case 4:return Znt(r,t,e);case 5:return Jnt(r,t);case 6:return Qnt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Lnt(r){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${r.texture2D}(textureSampler, uv).r;
    }
  `}function znt(r){return`
    void setOutput(float val) {
      ${r.output} = vec4(val, 0, 0, 0);
    }
  `}function Bnt(r){return`
    void setOutput(vec4 val) {
      ${r.output} = val;
    }
  `}function Vnt(r){return`${r.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${r.varyingFs} vec2 resultUV;
    ${r.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);
 
    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };
 
    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };
 
    uniform float NAN;
    ${r.defineSpecialNaN}
    ${r.defineSpecialInf}
    ${r.defineRound}
 
    int imod(int x, int y) {
      return x - y * (x / y);
    }
 
    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }
 
    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }
 
    ${Gnt}
    ${Wnt}
    ${Unt}
  `}var Gnt=`
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);
}
`,Wnt=`
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);
}
`,Unt=`
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);
}
`,Hnt=`
  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 DL(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function qnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${n[1]}.0);
      }
    `:n[1]===1?e?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${n[0]}.0);
      }
    `:e?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
    }
  `}function Knt(r,t,e){return t[0]===1?e?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?e?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:e?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function jnt(r,t,e){if(e)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
 
      int b = index / texelsInBatch;
      index -= b * texelsInBatch;
 
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;
 
      return ivec3(b, r, c);
    }
  `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;
 
      int b = index / ${s};
      index -= b * ${s};
 
      int r = 2 * (index / ${o});
      int c = imod(index, ${o}) * 2;
 
      return ivec3(b, r, c);
    }
  `}function Xnt(r,t,e){if(e)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${yp(["r","c","d"],r)}
    return ivec3(r, c, d);
  }
`;let n=ki(["r","c","d"],r);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec3(r, c, d);
    }
  `}function Ynt(r,t,e){if(e)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
 
      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];
 
      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;
 
      int b = index / texelsInBatch;
      index -= b * texelsInBatch;
 
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;
 
      return ivec4(b2, b, r, c);
    }
  `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l<r.length-1;l++)i*=r[r.length-l-1],a=`
      int b${l} = index / ${i};
      index -= b${l} * ${i};
    `+a,u=`b${l}, `+u;return`
    ivec${r.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;
 
      ${a}
 
      int b = index / ${s};
      index -= b * ${s};
 
      int r = 2 * (index / ${o});
      int c = imod(index, ${o}) * 2;
 
      return ivec${r.length}(${u});
    }
  `}function Znt(r,t,e){if(e)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${yp(["r","c","d","d2"],r)}
      return ivec4(r, c, d, d2);
    }
  `;let n=ki(["r","c","d","d2"],r);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function Jnt(r,t){let e=ki(["r","c","d","d2","d3"],r);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));
 
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
 
      ${e}
 
      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function Qnt(r,t){let e=ki(["r","c","d","d2","d3","d4"],r);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
 
      ${e}
 
      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function tot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
      }
    `;let o=Math.ceil(r[1]/2);return e?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
 
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;
 
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
 
      int index = resTexRC.x * ${n[1]} + resTexRC.y;
      int r = 2 * (index / ${o});
      int c = imod(index, ${o}) * 2;
 
      return ivec2(r, c);
    }
  `}function eot(r,t,e){return y.arraysEqual(r,t)?e?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:r[1]===1?e?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:r[0]===1?e?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:e?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${r[1]};
      int c = index - r * ${r[1]};
      return ivec2(r, c);
    }
  `}function bp(r){return`offset${r}`}function rot(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=We();return`
    vec4 ${e}() {
      return ${n.texture2D}(${t}, halfCR);
    }
  `}function not(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
      float ${n}() {
        return sampleTexture(${e}, halfCR);
      }
    `;let i=bp(e);if(t)return`
    float ${n}() {
      vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], ${i});
      return sampleTexture(${e}, uv);
    }
  `;let[a,u]=r.shapeInfo.texShape;return`
    float ${n}() {
      vec2 uv = uvFromFlat(${a}, ${u}, ${i});
      return sampleTexture(${e}, uv);
    }
  `}function oot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=We();if(t)return`
    vec4 ${n}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${e}, uv);
    }
  `;let i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${s.texture2D}(${e}, uv);
    }
  `}function sot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${Rd(r)}
      }
    `;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return`
      float ${n}(int index) {
        return sampleTexture(${e}, halfCR);
      }
    `;let a=bp(e);return i===1?t?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / float(${e}TexShape[0]));
        return sampleTexture(${e}, uv);
      }
    `:`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${s}.0);
        return sampleTexture(${e}, uv);
      }
    `:s===1?t?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${a}) + 0.5) / float(${e}TexShape[1]), 0.5);
        return sampleTexture(${e}, uv);
      }
    `:`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${a}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${e}, uv);
      }
    `:t?`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], index + ${a});
      return sampleTexture(${e}, uv);
    }
  `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${s}, ${i}, index + ${a});
      return sampleTexture(${e}, uv);
    }
  `}function iot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=We();if(s!=null&&y.arraysEqual(e,s))return t?`
      vec4 ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
 
        return ${u.texture2D}(${n}, uv);
      }
    `:`
      vec4 ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${i}.0);
 
        return ${u.texture2D}(${n}, uv);
      }
    `;if(t)return`
    vec4 ${o}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${u.texture2D}(${n}, uv);
    }
  `;let l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(e[1]/2);return`
    vec4 ${o}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
      return ${u.texture2D}(${n}, uv);
    }
  `}function aot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return`
      float ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `;let m=s[0],f=s[1];return`
    float ${o}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
      return sampleTexture(${n}, uv);
    }
  `}let{newShape:i,keptDims:a}=y.squeezeShape(e),u=i;if(u.length<e.length){let m=Fd(r,u),f=["row","col"];return`
      ${$d(m,t)}
      float ${o}(int row, int col) {
        return ${o}(${Od(f,a)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
        ${Rd(r)}
      }
    `;let l=s[0],c=s[1],p=bp(n);return c===1?t?`
      float ${o}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${o}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${n}, uv);
    }
  `:l===1?t?`
      float ${o}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${o}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${n}, uv);
    }
  `:t?`
      float ${o}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${n}Shape[1] + col + ${p};
        vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
        return sampleTexture(${n}, uv);
      }
    `:`
  float ${o}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${e[1]} + col + ${p};
    vec2 uv = uvFromFlat(${l}, ${c}, index);
    return sampleTexture(${n}, uv);
  }
`}function lot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(e[0]===1){let m=e.slice(1),f=[1,2],d=Fd(r,m),h=["b","row","col"];return`
        ${AL(d,t)}
        vec4 ${o}(int b, int row, int col) {
          return ${o}(${Od(h,f)});
        }
      `}let a=We();if(t)return`
    vec4 ${o}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${a.texture2D}(${n}, uv);
    }
  `;let u=i[0],l=i[1],c=Math.ceil(e[2]/2),p=c*Math.ceil(e[1]/2);return`
    vec4 ${o}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${u}, ${l}, ${p}, ${c}, b, row, col);
      return ${a.texture2D}(${n}, uv);
    }
  `}function uot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[1]*e[2],i=e[2],{newShape:a,keptDims:u}=y.squeezeShape(e),l=a;if(l.length<e.length){let h=Fd(r,l),g=["row","col","depth"];return`
        ${$d(h,t)}
        float ${o}(int row, int col, int depth) {
          return ${o}(${Od(g,u)});
        }
      `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${i}, 1)));
        ${Rd(r)}
      }
    `;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return t?`
      float ${o}(int row, int col, int depth) {
        int stride1 = ${n}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
        float ${o}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${m}.0, ${p}.0);
          return sampleTexture(${n}, uv);
        }
      `;if(m===i&&f==null)return t?`
      float ${o}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${o}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
      return sampleTexture(${n}, uv);
    }
  `;let d=bp(n);return t?`
    float ${o}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${n}Shape[1] * ${n}Shape[2];
      int stride1 = ${n}Shape[2];
      int index = row * stride0 + col * stride1 + depth + ${d};
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
      return sampleTexture(${n}, uv);
    }
    `:`
      float ${o}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${i} + depth + ${d};
        vec2 uv = uvFromFlat(${p}, ${m}, index);
        return sampleTexture(${n}, uv);
      }
  `}function cot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=We();if(t)return`
    vec4 ${n}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${e}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${e}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${e}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${o.texture2D}(${e}, uv);
    }
  `;let s=r.shapeInfo.logicalShape,i=s.length,a=r.shapeInfo.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=Math.ceil(s[i-1]/2),m=p*Math.ceil(s[i-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<i-1;h++)f=`int b${h}, `+f,m*=s[i-h-1],d=`b${h} * ${m} + `+d;return`
    vec4 ${n}(${f}) {
      int index = ${d};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
      return ${o.texture2D}(${e}, uv);
    }
  `}function pot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[3],i=e[2]*s,a=e[1]*i,{newShape:u,keptDims:l}=y.squeezeShape(e);if(u.length<e.length){let b=Fd(r,u),w=["row","col","depth","depth2"];return`
      ${$d(b,t)}
      float ${o}(int row, int col, int depth, int depth2) {
        return ${o}(${Od(w,l)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${a}, ${i}, ${s}, 1)));
        ${Rd(r)}
      }
    `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1],d=`int stride2 = ${n}Shape[3];`,h=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(f===a&&c==null)return t?`
      float ${o}(int row, int col, int depth, int depth2) {
        ${d}
        ${h}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${i}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(f===s&&c==null)return t?`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${e[1]*e[2]}, ${e[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${n}, uv);
      }
    `;let x=bp(n);return t?`
    float ${o}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${d}
      ${h}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${x});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${o}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${a} + col * ${i} +
          depth * ${s} + depth2;
      vec2 uv = uvFromFlat(${m}, ${f}, index + ${x});
      return sampleTexture(${n}, uv);
    }
  `}function mot(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,i=t[2]*s,a=t[1]*i,{newShape:u,keptDims:l}=y.squeezeShape(t);if(u.length<t.length){let h=Fd(r,u),g=["row","col","depth","depth2","depth3"];return`
      ${$d(h)}
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        return ${n}(${Od(g,l)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${a}, ${i}, ${s}, ${o})) +
          depth3;
        ${Rd(r)}
      }
    `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===a&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${s}, ${o}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(f===o&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${e}, uv);
      }
    `;let d=bp(e);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 * ${i} + depth * ${s} +
          depth2 * ${o} + depth3 + ${d};
      vec2 uv = uvFromFlat(${m}, ${f}, index);
      return sampleTexture(${e}, uv);
    }
  `}function fot(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=y.squeezeShape(t);if(o.length<t.length){let g=Fd(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
      ${$d(g)}
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${n}(${Od(x,s)});
      }
    `}let i=t[5],a=t[4]*i,u=t[3]*a,l=t[2]*u,c=t[1]*l;if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${l}, ${u}, ${a})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${Rd(r)}
      }
    `;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${l}, ${u}, ${a}, ${i})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${f}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(d===i&&p==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${f}.0);
        return sampleTexture(${e}, uv);
      }
    `;let h=bp(e);return`
    float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${l} + depth * ${u} +
          depth2 * ${a} + depth3 * ${i} + depth4 + ${h};
      vec2 uv = uvFromFlat(${f}, ${d}, index);
      return sampleTexture(${e}, uv);
    }
  `}function Rd(r){let t=r.name,e=y.sizeFromShape(r.shapeInfo.logicalShape);return e<2?`return ${t};`:`
    for (int i = 0; i < ${e}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function dot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=_L(r.shapeInfo.logicalShape,t.logicalShape),u=zt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`
`);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(h&&!x)i===1?f=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:f=`
        return vec4(outputValue.x);
      `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f="return vec4(outputValue.x);":a.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${o}() {
      ${u} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${n}(${m});
      ${f}
    }
  `}function hot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return`
      float ${o}() {
        return sampleTexture(${e}, resultUV);
      }
    `;let l=zt(u),c=_L(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
    float ${o}() {
      ${l} coords = getOutputCoords();
      ${m}
      return get${n}(${d});
    }
  `}function zt(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function zw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.length<s||i;return{useSqueezeShape:u,uniformShape:u?a:t,keptDims:o}}function Fd(r,t){let e=JSON.parse(JSON.stringify(r));return e.shapeInfo.logicalShape=t,e}function Od(r,t){return t.map(e=>r[e]).join(", ")}function RL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=EL(o,i,t),u=zT(r.gl,a),l=r.createProgram(u);return L().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},n1(r,t,l)))}function n1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,"NAN",!1),L().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,"outShape",c),a=r.getUniformLocation(e,"outShapeStrides",c),i=r.getUniformLocation(e,"outTexShape",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function $L(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function FL(r,t,e,n,o){t.program.enableShapeUniforms||($L(t.inShapeInfos,e),$L([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u<e.length;++u){let l=e[u],{uniform:c,offset:p,shape:m,texShape:f}=t.variablesLocations[u];if(m){let{uniformShape:d}=zw(t.program.packedInputs,l.shape,l.texData.texShape);switch(d.length){case 1:r.gl.uniform1iv(m,new Int32Array(d));break;case 2:r.gl.uniform2iv(m,new Int32Array(d));break;case 3:r.gl.uniform3iv(m,new Int32Array(d));break;case 4:r.gl.uniform4iv(m,new Int32Array(d));break;default:break}}if(f&&r.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(y.sizeFromShape(l.shape)<2)r.gl.uniform1f(c,l.uniformValues[0]);else{let d=l.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),r.gl.uniform1fv(c,d)}continue}l.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,l.texData.slice.flatOffset),r.setInputMatrixTexture(l.texData.texture.texture,c,u)}}let a=t.outShapeLocation;if(a)switch(n.shape.length){case 1:r.gl.uniform1iv(a,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(a,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(a,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(a,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let u=y.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:r.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:r.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}if(t.outTexShapeLocation&&r.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&o)for(let u=0;u<t.program.customUniforms.length;++u){let l=t.program.customUniforms[u],c=t.customUniformLocations[u],p=o[u];if(l.type==="float")r.gl.uniform1fv(c,p);else if(l.type==="vec2")r.gl.uniform2fv(c,p);else if(l.type==="vec3")r.gl.uniform3fv(c,p);else if(l.type==="vec4")r.gl.uniform4fv(c,p);else if(l.type==="int")r.gl.uniform1iv(c,p);else if(l.type==="ivec2")r.gl.uniform2iv(c,p);else if(l.type==="ivec3")r.gl.uniform3iv(c,p);else if(l.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}r.executeProgram()}function OL(r,t,e){let n="";t.concat(e).forEach(i=>{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=zw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${L().getNumber("WEBGL_VERSION")}`,s}function de(r){return L().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Bw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Zu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?yp(["r","c","d"],t):ki(["r","c","d"],t)}
        return ivec3(r, c, d);
      }
 
      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
 
        vec4 result = vec4(0.);
 
        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }
 
        ${e.output} = result;
      }
    `}};var Vw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Zu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?yp(["r","c","d"],t):ki(["r","c","d"],t)}
        return ivec3(r, c, d);
      }
 
      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
 
        vec4 result = vec4(0.);
 
        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }
 
        ${e.output} = result;
      }
    `}};var Gw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Jr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`
      ${Lw}
 
      void main() {
        float x = getAAtOutCoords();
        ${e.output} = encode_float(x);
      }
    `}};var Ww=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`
      ${Lw}
 
      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${e.output} = encode_float(x);
      }
    `}};var yot={R:0,G:1,B:2,A:3},cg=class{constructor(t,e=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let o=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let i="";for(let a=0;a<n.length;a++){let u=n[a];i+=`
          if(offset == ${a}) {
            result = values[${yot[u]}];
          }`}this.userCode=`
      ${this.enableShapeUniforms?Ad():Ed(t)}
 
      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${n.length});
 
        flatIndex = idiv(flatIndex, ${n.length}, 1.);
 
        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${o.texture2D}(A, uv);
          ${i}
        }
        ${o.output} = vec4(${s}, 0., 0., 0.);
      }
    `}};var Uw=class{constructor(t,e=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let o="",s="result";e&&(s="floor(result * 255. + 0.5)");for(let i=0;i<=1;i++)for(let a=0;a<=1;a++){let u=i*2+a;o+=`
          localCoords = coords;
          if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${t[2]}`}) {
          localCoords[2] += ${a};
          if (localCoords[1] + ${i} < ${this.enableShapeUniforms?"outShape[1]":`${t[1]}`}) {
            localCoords[1] += ${i};
 
            flatIndex = getFlatIndex(localCoords);
            offset = imod(flatIndex, 4);
 
            flatIndex = idiv(flatIndex, 4, 1.);
 
            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${n.texture2D}(A, uv);
 
            if (offset == 0) {
              result[${u}] = values[0];
            } else if (offset == 1) {
              result[${u}] = values[1];
            } else if (offset == 2) {
              result[${u}] = values[2];
            } else {
              result[${u}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?Ad():Ed(t)}
 
        void main() {
          ivec3 coords = getOutputCoords();
 
          vec4 result = vec4(0.);
          int flatIndex, r, c, offset;
          ivec3 localCoords;
          vec2 uv;
          vec4 values;
 
          ${o}
 
          ${n.output} = ${s};
        }
    `}};var w1={};Kt(w1,{bindVertexProgramAttributeStreams:()=>m1,createBufferFromOutputTexture:()=>h1,createFloat16MatrixTexture:()=>l1,createFloat16PackedMatrixTexture:()=>p1,createFloat32MatrixTexture:()=>a1,createIndexBuffer:()=>i1,createPackedMatrixTexture:()=>c1,createUnsignedBytesMatrixTexture:()=>u1,createVertexBuffer:()=>s1,createVertexShader:()=>o1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>x1,downloadFloat32MatrixFromBuffer:()=>g1,downloadMatrixFromPackedOutputTexture:()=>b1,downloadPackedMatrixFromBuffer:()=>y1,getInternalFormatForFloat16MatrixTexture:()=>qw,getInternalFormatForFloat16PackedMatrixTexture:()=>Xw,getInternalFormatForFloat32MatrixTexture:()=>Hw,getInternalFormatForPackedMatrixTexture:()=>jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Kw,uploadDenseMatrixToTexture:()=>f1,uploadPixelDataToTexture:()=>d1});function o1(r){let t=We(),e=`${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;
 
    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return LT(r,e)}function s1(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return GT(r,t)}function i1(r){let t=new Uint16Array([0,1,2,2,1,3]);return WT(r,t)}function pg(r,t,e,n,o,s){HT(t,e);let i=UT(r),a=r.TEXTURE_2D;return ht(r,()=>r.bindTexture(a,i)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),L().getNumber("WEBGL_VERSION")===1?ht(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):ht(r,()=>r.texStorage2D(a,1,n,t,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function Hw(r){return r.internalFormatFloat}function a1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,Hw(n),n.textureFormatFloat,r.FLOAT)}function qw(r){return r.internalFormatHalfFloat}function l1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,qw(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Kw(r){return r.downloadTextureFormat}function u1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,Kw(n),r.RGBA,r.UNSIGNED_BYTE)}function jw(r){return r.internalFormatPackedFloat}function c1(r,t,e,n){let[o,s]=Sa(t,e);return pg(r,o,s,jw(n),r.RGBA,r.FLOAT)}function Xw(r){return r.internalFormatPackedHalfFloat}function p1(r,t,e,n){let[o,s]=Sa(t,e);return pg(r,o,s,Xw(n),r.RGBA,n.textureTypeHalfFloat)}function m1(r,t,e){return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),Ow(r,t,"clipSpacePos",e,3,20,0)&&Ow(r,t,"uv",e,2,20,12)}function f1(r,t,e,n,o,s){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t));let i,a,u;o instanceof Uint8Array?(i=new Uint8Array(e*n*4),a=r.UNSIGNED_BYTE,u=r.RGBA):(i=new Float32Array(e*n*4),a=r.FLOAT,u=s.internalFormatPackedFloat),i.set(o),L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e,n,r.RGBA,a,i)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,u,e,n,0,r.RGBA,a,i)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function d1(r,t,e){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t)),e.data instanceof Uint8Array?L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e.width,e.height,r.RGBA,r.UNSIGNED_BYTE,e.data)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,e.width,e.height,0,r.RGBA,r.UNSIGNED_BYTE,e.data)):L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,e)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function h1(r,t,e,n){let o=r.createBuffer();ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let a=4*4*t*e;return ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function g1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function x1(r,t,e,n){let[o,s]=xp(t,e),i=4,a=new Uint8Array(IL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function y1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(CL(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function b1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var wp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber("WEBGL_VERSION");if(t!=null?(this.gl=t,FT(e,t)):this.gl=Yn(e),t=this.gl,L().getNumber("WEBGL_VERSION")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension("OES_vertex_array_object");if(s==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),L().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",i="OES_texture_half_float";if(this.textureFloatExtension=Nd(this.gl,s),Zn(this.gl,i))this.textureHalfFloatExtension=Nd(this.gl,i);else if(L().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Zn(this.gl,o))this.colorBufferHalfFloatExtension=Nd(this.gl,o);else if(L().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Zn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Zn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=s1(this.gl),this.indexBuffer=i1(this.gl),this.framebuffer=qT(this.gl),this.textureConfig=ag(this.gl,this.textureHalfFloatExtension)}get debug(){return L().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),a1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),l1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),u1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),d1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),f1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),p1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),c1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Pw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>x1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return y1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return g1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=h1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>b1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=o1(e));let n=BT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),VT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&lg(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),m1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&lg(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?KT(this.gl,t,e):jT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),XT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Sa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&lg(this.gl,this.program),kd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Nd(this.gl,L().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(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=bot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),ug(this.gl,t,this.framebuffer),this.debug&&kd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ug(this.gl,this.outputTexture,this.framebuffer),this.debug&&kd(this.gl)):Pw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;ug(o,t,this.framebuffer),this.debug&&kd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function bot(r){let t=0;for(;t<r.length&&r[t]();++t);return t-1}var{addImpl:PL,bincountImpl:Yw,bincountReduceImpl:ML,bitwiseAndImpl:LL,castImpl:zL,ceilImpl:BL,concatImpl:VL,equalImpl:GL,expImpl:WL,expm1Impl:UL,floorImpl:HL,gatherNdImpl:qL,gatherV2Impl:KL,greaterImpl:jL,greaterEqualImpl:XL,lessImpl:YL,lessEqualImpl:ZL,linSpaceImpl:JL,logImpl:QL,maxImpl:tz,maximumImpl:ez,minimumImpl:rz,multiplyImpl:nz,negImpl:oz,notEqualImpl:sz,prodImpl:iz,raggedGatherImpl:az,raggedRangeImpl:lz,raggedTensorToTensorImpl:uz,rangeImpl:cz,rsqrtImpl:pz,scatterImpl:mz,sigmoidImpl:fz,simpleAbsImpl:Zw,sliceImpl:dz,sparseFillEmptyRowsImpl:hz,sparseReshapeImpl:gz,sparseSegmentReductionImpl:Jw,sqrtImpl:xz,staticRegexReplaceImpl:yz,stridedSliceImpl:bz,stringNGramsImpl:wz,stringSplitImpl:Iz,stringToHashBucketFastImpl:Cz,subImpl:vz,tileImpl:Sz,topKImpl:Nz,transposeImpl:Ip,uniqueImpl:kz}=Nw;function I1(r,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${r}.${e}`)}function er(r,t){return t===1?[r]:I1(r,t)}function Tz(r,t){if(r===1)return"rc";let e="";for(let n=0;n<r;n++)e+=t[n],n<r-1&&(e+=",");return e}var Qw=class{constructor(t){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.enableShapeUniforms=de(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let e=er("rc",this.rank),n=zt(this.rank),o=this.getOutOfBoundsCondition(e),s=this.getSetup(e),i=this.getOutput(e);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();
 
          if(${o}) {
            setOutput(vec4(0));
          } else {
            ${s}
 
            setOutput(vec4(${i}));
          }
        }
      `}}getSourceCoordsArr(t){let e=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let i=2;i<this.rank;i++)s=`${t[t.length-1-i]},`+s;e.push(s)}return e}getOutOfBoundsCondition(t){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n<this.rank;n++)e+=`${t[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(e+="||");return e}getSetup(t){if(this.rank===1)return"";let e=t.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],o=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
      int r = ${e[0]};
      int c = ${e[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;
 
      bool cEdge = cp1 >= ${n};
      bool rEdge = rp1 >= ${o};
    `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),
            cEdge ? 0. : getA(${e[1]}),
            rEdge ? 0. : getA(${e[2]}),
            rEdge || cEdge ? 0. : getA(${e[3]})`}};var Pd=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
        ${s}
        ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);
 
          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
 
          result[${o}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${o>0?"}":""}
      `}this.userCode=`
      ${wot(e,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?Ad():Ed(t)}
 
      void main() {
        ivec3 rc = getOutputCoords();
 
        vec4 result = vec4(0.);
 
        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms?"outShape[1]":t[1]};
        int cols = ${this.enableShapeUniforms?"outShape[2]":t[2]};
 
        ${n}
 
        setOutput(result);
      }
    `}};function wot(r,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?TL(["r","c","d"],"inputShape"):ki(["r","c","d"],r)}
      return ivec3(r, c, d);
    }
  `}var tI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=Ez(e,n),s=Az(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=_z(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===zr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===zr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===zr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===zr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===zr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=Ez(n,o),i=Az(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=_z(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Iot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function _z(r,t,e,n,o){let s=Cot(t,n),i;if(o){let[u,l]=Sa(r[0],r[1]);i=u*l}else{let[u,l]=xp(r[0],r[1]);i=u*l}let a=Iot(e,s);return i*a}function Cot(r,t){switch(r){case zr.PACKED_2X2_FLOAT32:return jw(t);case zr.PACKED_2X2_FLOAT16:return Xw(t);case zr.UNPACKED_FLOAT32:return Hw(t);case zr.UNPACKED_FLOAT16:return qw(t);case zr.PACKED_4X1_UNSIGNED_BYTE:return Kw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function vot(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?zr.PACKED_2X2_FLOAT32:zr.UNPACKED_FLOAT32:r?zr.PACKED_2X2_FLOAT16:zr.UNPACKED_FLOAT16}function Ez(r,t){if(r===Jr.UPLOAD)return zr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return vot(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return zr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Az(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Br=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${e}
      }
 
      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);
 
        setOutput(y);
      }
    `}},yr="if (isnan(x)) return x;",Dz="return x;",C1="return abs(x);";var $z="return (x >= 0.0) ? x : (exp(x) - 1.0);",Rz=yr+`
  return (x < 0.0) ? 0.0 : x;
`,Fz=yr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Na="return x;",Oz="return 1.0 / (1.0 + exp(-1.0 * x));";var Mz="return x;",Lz=`
  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;
`,zz=`
  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;
`,Bz=`
  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;
`,Vz="return 1.0 / (1.0 + exp(-1.0 * x));",Fn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${e}
      }
 
      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);
 
        setOutput(y);
      }
    `}};var eI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er("rc",e),o=zt(e),s=Tz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
      void main() {
        ${o} rc = getOutputCoords();
        vec4 packedInput = getA(${s});
 
        setOutput(getChannel(packedInput, ${a}));
      }
    `}};var Not=jr.whereImpl,kot=1e-7,Tot=1e-4,rI={};function _ot(r){return r in rI||(rI[r]={}),rI[r]}var Eot=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Aot=600;function Dot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Aot/1024/1024}var Qu=class extends Uo{nextDataId(){return Qu.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof wp)e=t;else{let n=Yn(L().getNumber("WEBGL_VERSION"),t);e=new wp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Yn(L().getNumber("WEBGL_VERSION"));e=new wp(n),this.binaryCache=_ot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tI(this.gpgpu),this.numMBBeforeWarning=Dot(),this.texData=new Da(this,Wn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Td(e),c=new cg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Fn(a,Na):m=new Br(a,Na);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Fn(o,Na):d=new Br(o,Na);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().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(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...ig(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Wn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Fn(s,Na):f=new Br(s,Na);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Wn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e<t.length;e++){let n=t[e];if(!MT(n))throw L().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:n,isPacked:o}=this.texData.get(t),s=y.sizeFromShape(e);if(L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(t),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture,...ig(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let i=L().getBool("WEBGL_PACK")&&o===!0,a=i?Td(e):e,u=i?new Ww(a):new Gw(a),l=this.runWebGLProgram(u,[{shape:a,dtype:n,dataId:t}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=Eot){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<e)}getGPGPUContext(){return this.gpgpu}where(t){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let e=t.dataSync();return Not(t.shape,e)}packedUnaryOp(t,e,n){let o=new Fn(t.shape,e),s=this.compileAndRun(o,[t],n);return Wn().makeTensorFromTensorInfo(s)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let o=Zw(this.texData.get(t.dataId).values);return this.makeOutput(t.shape,t.dtype,o)}if(L().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,C1,t.dtype);let e=new Br(t.shape,C1),n=this.compileAndRun(e,[t]);return Wn().makeTensorFromTensorInfo(n)}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Wn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new eI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Qw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[Vl(t.shape),...Gl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Vl(e),...Gl(e)],i=new Pd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Td(s),u;o?u=new Vw(a):u=new Bw(a);let l=!0,c=[e!=null?e:ig(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Zu.DENSE){let x=i!=null?i:ig(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Ju(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=OL(t,c,p),f=this.getAndSaveBinary(m,()=>RL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||FL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(ft(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?kot:Tot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=YT(n,u),e.texShape=p),s!=null){let m=Td(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Sa(p[0],p[1])),u?f=new Uw(m,g):f=new cg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,I,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=$ot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await kh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Fw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=n1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Wn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Wn().makeTensorFromDataId(l,e,n,u)}};Qu.nextDataId=0;function $ot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<e.length;++n)e[n]=Math.round(r[n]);return e}else throw new Error(`Unknown dtype ${t}`)}var Gz="4.7.0";function Wz(){L().set("WEBGL_FORCE_F16_TEXTURES",!0)}Cu.isBrowser()&&im("webgl",()=>new Qu,2);var JDe={forceHalfFloat:Wz};var Md=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;var On=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${t}
      }
 
      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}};var Qn=`
  result.r = isNaN.r ? NAN : result.r;
  result.g = isNaN.g ? NAN : result.g;
  result.b = isNaN.b ? NAN : result.b;
  result.a = isNaN.a ? NAN : result.a;
`;var Jn=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=de(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(i=`
          ${zt(s)} coords = getOutputCoords();
        `,s===1)this.enableShapeUniforms?i+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:i+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let u=er("coords",s);this.enableShapeUniforms?i+=`
            bool nextRowOutOfBounds =
              (${u[s-2]} + 1) >= outShape[${s} - 2];
            bool nextColOutOfBounds =
              (${u[s-1]} + 1) >= outShape[${s} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:i+=`
            bool nextRowOutOfBounds =
              (${u[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${u[s-1]} + 1) >= ${this.outputShape[s-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${t}
      }
 
      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();
 
        vec4 result = binaryOperation(a, b);
        ${i}
 
        setOutput(result);
      }
    `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Uz={kernelName:bo,backendName:"webgl",kernelFunc:rr};function Pn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var Hz={kernelName:zp,backendName:"webgl",kernelFunc:Pn};var v1="return (a < 0.) ? b * a : a;",S1=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jn(S1,o.shape,i.shape):new On(v1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var qz={kernelName:vs,backendName:"webgl",kernelFunc:Rot};var N1="return (a < 0.) ? b * a : a;",k1=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Fot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jn(k1,n.shape,o.shape):new On(N1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var Kz={kernelName:zs,backendName:"webgl",kernelFunc:Fot};var Vo="if (isnan(x)) return x;";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Fn(i.shape,t):c=new Br(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ue({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new On(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Pn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Jn(t,u.shape,l.shape,e):f=new On(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Wl(r,t=!1){if(r==="linear")return t?Mz:Dz;if(r==="relu")return t?zz:Rz;if(r==="elu")return t?Lz:$z;if(r==="relu6")return t?Bz:Fz;if(r==="prelu")return t?k1:N1;if(r==="leakyrelu")return t?S1:v1;if(r==="sigmoid")return t?Vz:Oz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Ld=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=de(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${a}
        }`:l?g=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${a}
        }`:g=`vec4 activation(vec4 x) {
          ${a}
        }`,x="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",I="rc.x";t[0]<e[0]?w=`imod(rc.x, ${t[0]})`:e[0]<t[0]&&(I=`imod(rc.x, ${e[0]})`),this.userCode=`
      ${g}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${p}.0;
 
      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        int batchA = ${w};
        int batchB = ${I};
        for (int i = 0; i < ${p}; i++) {
          vec4 a = getMatrixA(batchA, ${m});
          vec4 b = getMatrixB(batchB, ${f});
 
          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${d[0]} * ${h[0]});
          result += (${d[1]} * ${h[1]});
        }
        return result;
      }
 
      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);
 
        ${b}
 
        ${x}
 
        setOutput(result);
      }
    `}};var T1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},mg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${t}
      }
 
      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}};var jz="return a * b;";function fg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new mg(T1.REAL,n.shape,o.shape),c=new mg(T1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Pn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=nz(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Jn(jz,n.shape,o.shape):i=new On(jz,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var Xz={kernelName:Os,backendName:"webgl",kernelFunc:fg};function Yz(r,t,e){let n=[Vl(r.shape),...Gl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Vl(t),...Gl(t)],i=new Pd(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Ju(o.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?Yz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var Zz={kernelName:Ui,backendName:"webgl",kernelFunc:rt};var dg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
      float getValue(int batch, int inIdx) {
        ${c}
        return getX(batch, inIdx);
      }
 
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};
 
        float sumValue = 0.0;
 
        for (int i = 0; i < ${a}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );
 
          ${l}
        }
 
        int inIdx = inOffset + ${a};
        if (${u===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
 
          ${l}
        } else if (${u===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);
 
          ${l}
        } else if (${u===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);
 
          ${l}
        }
        setOutput(sumValue);
      }
    `}};var nI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
      if (${e==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${e==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${u}(values, minMaxValue);
        if (${e==="min"} || ${e==="max"}) {
          minMaxValue = ${u}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,f="vec4";e==="all"?(a="1.0",m=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,f="bvec4"):e==="any"&&(a="0.0",m=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,f="bvec4");let d="";s%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${a};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
      float getValue(int batch, int inIdx) {
        ${d}
        return getX(batch, inIdx);
      }
 
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};
 
        vec4 minMaxValue = vec4(${a});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;
 
        for (int i = 0; i < ${c}; i += 4) {
          int inIdx = inOffset + i;
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );
 
          ${m}
        }
 
        int inIdx = inOffset + ${c};
        if (${p===1}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );
 
          ${m}
        } else if (${p===2}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );
 
          ${m}
        } else if (${p===3}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );
 
          ${m}
        }
        setOutput(${l});
      }
    `}};function Pot(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function to(r,t,e,n){let o=Pot(r.shape),s=r;for(let i=0;i<o.length;i++){let{inSize:a,windowSize:u,outSize:l}=o[i],c,p;e==="mean"?c=i===0?new dg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},a):new dg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l}):c=new nI({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},e),p=s,s=n.runWebGLProgram(c,[s],t),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var oI=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[e[i]];this.outputShape=n,this.rank=n.length;let o=zt(this.rank),s=Mot(e);this.userCode=`
    void main() {
      ${o} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function Mot(r){let t=r.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o<r.length;o++)n[r[o]]=e[o];return n.join()}var sI=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(t.length);for(let c=0;c<n.length;c++)n[c]=t[e[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=I1("rc",this.rank),i=new Array(this.rank);for(let c=0;c<e.length;c++)i[e[c]]=s[c];let a=`vec2(${i.slice(-2).join()})`,u=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`
    void main() {
      ${o} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${u}) {
        result[1] = ${l};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${u}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function tc(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sI(r.shape,t):new oI(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function Jz(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=tc(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=xc(r.dtype),w=to(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function Cp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return Jz(o,s,i,e)}var Qz={kernelName:ri,backendName:"webgl",kernelFunc:Cp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c<u.length;c++)u[c]=o.shape[s[c]];let l;if(i.shouldExecuteOnCPU([o])){let p=i.texData.get(o.dataId).values,m=Ip(p,o.shape,o.dtype,s,u);l=i.makeTensorInfo(u,o.dtype);let f=i.texData.get(l.dataId);f.values=m}else l=tc(o,s,i);return l}var t3={kernelName:uo,backendName:"webgl",kernelFunc:Pe};var _1=1e3;function vp({a:r,b:t,transposeA:e,transposeB:n,backend:o,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:u=null}){let l=r.shape.length,c=t.shape.length,p=e?r.shape[l-2]:r.shape[l-1],m=n?t.shape[c-1]:t.shape[c-2],f=e?r.shape[l-1]:r.shape[l-2],d=n?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),I=Hr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Wl(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>_1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,at=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(ot));let it=d===1?2:1,mt=nt;at&&(mt=rt({inputs:{x:nt},backend:o,attrs:{shape:[P,1,V]}}),F.push(mt));let gt=fg({inputs:{a:ot,b:mt},backend:o});X=Cp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Ld(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return vp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var e3={kernelName:Zi,backendName:"webgl",kernelFunc:Lot};var r3="return abs(x);";function zot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fn(n.shape,r3):o=new Br(n.shape,r3),e.runWebGLProgram(o,[n],n.dtype)}var n3={kernelName:$i,backendName:"webgl",kernelFunc:zot};var Bot=yr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,Vot=It({opSnippet:Bot}),o3={kernelName:qo,backendName:"webgl",kernelFunc:Vot};var Got=yr+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Wot=It({opSnippet:Got}),s3={kernelName:Ko,backendName:"webgl",kernelFunc:Wot};var i3="return a + b;",Uot=ue({opSnippet:i3,packedOpSnippet:i3,supportsComplex:!0,cpuKernelImpl:PL}),a3={kernelName:ao,backendName:"webgl",kernelFunc:Uot};var iI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}
 
        float result = ${o};
        setOutput(result);
      }
    `}};var aI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}
 
        vec4 result = ${o};
        setOutput(result);
      }
    `}};function lI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>L().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=lI({inputs:n.slice(0,u),backend:e}),c=lI({inputs:n.slice(u),backend:e});return lI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new aI(n[0].shape,s):new iI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var l3={kernelName:jo,backendName:"webgl",kernelFunc:lI};function Hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=to(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var u3={kernelName:Ra,backendName:"webgl",kernelFunc:Hot};function qot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=to(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var c3={kernelName:Fa,backendName:"webgl",kernelFunc:qot};var uI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${o};
 
        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);
 
        for (int i = 0; i < ${o}; i++) {
          int inIdx = ${u};
          float candidate = getA(batch, inIdx);
          if (candidate ${a} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}};var cI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=er("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`
        ${D} sourceLocR = ${D}(${c.join()}, 0);
        ++${c[u-1]};
        ${D} sourceLocG = ${D}(${c.join()}, 0);
        ++${c[u-2]};
        ${D} sourceLocA = ${D}(${c.join()}, 0);
        --${c[u-1]};
        ${D} sourceLocB = ${D}(${c.join()}, 0);
        --${c[u-2]};`}else m=u,p=`
        ${l} sourceLocR = coords;
        ++${c[u-1]};
        ${l} sourceLocG = coords;
        ++${c[u-2]};
        ${l} sourceLocA = coords;
        --${c[u-1]};
        ${l} sourceLocB = coords;
        --${c[u-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(D=>"int "+D),g=er("sourceLocR",m-1).concat("inIdx.r"),x=er("sourceLocG",m-1).concat("inIdx.g"),b=er("sourceLocB",m-1).concat("inIdx.b"),w=er("sourceLocA",m-1).concat("inIdx.a"),I=n==="max"?"greaterThan":"lessThan",N=o?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${x.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${w.join()})));`,E=`vec4(
            getAChannel(${g.join()}),
            hasNextCol ? getAChannel(${x.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?"":`
      float getBestIndicesAChannel(${h.join()}) {
        return getChannel(getBestIndicesA(${f.join()}),
                                          vec2(${f.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${h.join()}) {
        return getChannel(getA(${f.join()}),
                               vec2(${f.slice(-2).join()}));
      }
      ${A}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};
        bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};
        ${p}
        ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
          sourceLocB${d}, sourceLocA${d}) * ${e};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${E};
 
        for (int i = 0; i < ${e}; i++) {
          inIdx = srcIdx;
          ${N}
          vec4 candidate = ${E};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
 
          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function p3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new uI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=p3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function m3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new cI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=m3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function pI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=p3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return m3(r,t,n)}function Kot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=pI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var f3={kernelName:Ri,backendName:"webgl",kernelFunc:Kot};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=pI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var d3={kernelName:Fi,backendName:"webgl",kernelFunc:jot};var Xot=yr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,Yot=It({opSnippet:Xot}),h3={kernelName:Xo,backendName:"webgl",kernelFunc:Yot};var Zot=yr+"return log(x + sqrt(x * x + 1.0));",Jot=It({opSnippet:Zot}),g3={kernelName:Yo,backendName:"webgl",kernelFunc:Jot};var Qot=yr+`
  return atan(x);
`,tst=It({opSnippet:Qot}),x3={kernelName:Zo,backendName:"webgl",kernelFunc:tst};var est=Md+`
  return atan(a, b);
`,rst=`
  vec4 result = atan(a, b);
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+Qn+`
  return result;
`,nst=ue({opSnippet:est,packedOpSnippet:rst}),y3={kernelName:Qo,backendName:"webgl",kernelFunc:nst};var ost=yr+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelName:Jo,backendName:"webgl",kernelFunc:sst};var Ti=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch  * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
        const ivec2 strides = ivec2(${a}, ${u});
        const ivec2 pads = ivec2(${f}, ${d});
 
        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];
 
          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;
 
          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;
 
          for (int wR = 0; wR < ${p};
              wR += ${l}) {
            int xR = xRCorner + wR;
 
            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }
 
            for (int wC = 0; wC < ${m};
                wC += ${c}) {
              int xC = xCCorner + wC;
 
              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }
 
              float value = getX(batch, xR, xC, d);
 
              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${D} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let w="max",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(I="avgValue / max(count, 1.0)");let N=Math.floor(i/4)*4,E=i%4,A=`
      if (${h}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${w}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${a}, ${u});
      const ivec2 pads = ivec2(${f}, ${d});
      const float initializationValue = ${b};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
      float count = 0.0;
 
      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${t.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];
 
        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;
 
        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${b});
        float avgValue = 0.0;
        count = 0.0;
 
        for (int wR = 0; wR < ${p};
            wR += ${l}) {
          int xR = xRCorner + wR;
 
          if (xR < 0 || xR >= ${t.inHeight}) {
            continue;
          }
 
          for (int wC = 0; wC < ${N}; 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)
            );
 
            ${A}
          }
 
          int xC = xCCorner + ${N};
          if (${E===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );
 
            ${A}
          } else if (${E===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              initializationValue,
              initializationValue
            );
 
            ${A}
          } else if (${E===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              getValue(batch, xR, xC + 2 * ${c}, d),
              initializationValue
            );
 
            ${A}
          }
        }
        setOutput(${I});
      }
    `}},ec=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",I="0.0";if(w||(I="-1.0 / 1e-20"),n){let P=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${a}, ${u}, ${l});
        const ivec3 pads = ivec3(${g}, ${x}, ${b});
 
        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;
 
          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;
 
          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
 
          for (int wD = 0; wD < ${f};
              wD += ${c}) {
            int xD = xDCorner + wD;
 
            if (xD < 0 || xD >= ${t.inDepth}) {
              continue;
            }
 
            for (int wR = 0; wR < ${d};
                wR += ${p}) {
              int xR = xRCorner + wR;
 
              if (xR < 0 || xR >= ${t.inHeight}) {
                continue;
              }
 
              for (int wC = 0; wC < ${h};
                  wC += ${m}) {
                int xC = xCCorner + wC;
 
                if (xC < 0 || xC >= ${t.inWidth}) {
                  continue;
                }
 
                float value = getX(batch, xD, xR, xC, ch);
 
                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${P} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +
                      wR * ${h} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let N="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / max(count, 1.0)");let A=Math.floor(i/4)*4,D=i%4,F=`
      if (${w}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${N}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${a}, ${u}, ${l});
      const ivec3 pads = ivec3(${g}, ${x}, ${b});
      const float initializationValue = ${I};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
      float count = 0.0;
 
      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${t.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }
 
      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;
 
        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;
 
        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${I});
        float avgValue = 0.0;
        count = 0.0;
 
        for (int wD = 0; wD < ${f};
            wD += ${c}) {
          int xD = xDCorner + wD;
 
          if (xD < 0 || xD >= ${t.inDepth}) {
            continue;
          }
 
          for (int wR = 0; wR < ${d};
            wR += ${p}) {
            int xR = xRCorner + wR;
 
            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }
 
            for (int wC = 0; wC < ${A}; wC += 4) {
              int xC = xCCorner + wC * ${m};
 
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                getValue(batch, xD, xR, xC + 2 * ${m}, ch),
                getValue(batch, xD, xR, xC + 3 * ${m}, ch)
              );
 
              ${F}
            }
 
            int xC = xCCorner + ${A};
            if (${D===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );
 
              ${F}
            } else if (${D===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                initializationValue,
                initializationValue
              );
 
              ${F}
            } else if (${D===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                getValue(batch, xD, xR, xC + 2 * ${m}, ch),
                initializationValue
              );
 
              ${F}
            }
          }
        }
        setOutput(${E});
      }
    `}};function ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ni(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new Ti(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var w3={kernelName:ts,backendName:"webgl",kernelFunc:ist};function ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new ec(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var I3={kernelName:Oi,backendName:"webgl",kernelFunc:ast};var mI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`
      const ivec2 pads = ivec2(${c}, ${p});
      const float avgMultiplier = float(${m});
 
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
 
        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;
 
        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${u};
            wR += ${i}) {
          float dyR = float(dyRCorner + wR) / ${o}.0;
 
          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
 
          for (int wC = 0; wC < ${l};
            wC+= ${a}) {
            float dyC = float(dyCCorner + wC) / ${s}.0;
 
            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);
 
            float dyValue = getDy(b, idyR, idyC, d);
 
            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},fI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`
      const ivec3 pads = ivec3(${d}, ${h}, ${g});
      const float avgMultiplier = float(${x});
 
      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;
 
        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;
 
        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
 
        for (int wD = 0; wD < ${p};
            wD += ${u}) {
          float dyD = float(dyDCorner + wD) / ${s}.0;
 
          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);
 
          for (int wR = 0; wR < ${m};
              wR += ${l}) {
            float dyR = float(dyRCorner + wR) / ${i}.0;
 
            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);
 
            for (int wC = 0; wC < ${f};
                wC += ${c}) {
              float dyC = float(dyCCorner + wC) / ${a}.0;
 
              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);
 
              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
 
              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function lst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new fI(m);return e.runWebGLProgram(f,[o],i.dtype)}var C3={kernelName:Jl,backendName:"webgl",kernelFunc:lst};function ust(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ni([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new mI(c);return e.runWebGLProgram(p,[o],i.dtype)}var v3={kernelName:Zl,backendName:"webgl",kernelFunc:ust};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return vp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var S3={kernelName:es,backendName:"webgl",kernelFunc:cst};var dI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${a};
        float scale = ${u};
        float inv = scale * inversesqrt(variance + float(${i}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}};var hI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
      void main() {
        vec4 offset = ${a};
        vec4 scale = ${u};
 
        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();
 
        vec4 inv = scale * inversesqrt(variance + vec4(${i}));
 
        setOutput((x - mean) * inv + offset);
      }
    `}};var pst=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new hI(n.shape,o.shape,s.shape,c,p,u):new dI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},N3={kernelName:ys,backendName:"webgl",kernelFunc:pst};var gI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=mst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${E1[a]} = start[${a}] + coords.${E1[a]};`);o=`
        ${e} sourceLoc;
        ${e} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      void main() {
        ${o}
        setOutput(getSource(${n}));
      }
    `}},E1=["x","y","z","w","u","v"];function mst(r){if(r===1)return"sourceLoc";if(r<=6)return E1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var xI=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`
      result.x = ${i};
      if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
        ++${o[this.rank-1]};
        result.y = ${i};
        --${o[this.rank-1]};
      }
    `,u=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${t[this.rank-2]}) {
        ++${o[this.rank-2]};
        result.z = ${i};
        if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
          ++${o[this.rank-1]};
          result.w = ${i};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
      void main() {
        ${e} coords = getOutputCoords();
        ${e} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${a}
        ${u}
        setOutput(result);
      }
    `}};function fst(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function _i(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=dz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xI(u):new gI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),fst(o,a,u,e)}var k3={kernelName:qi,backendName:"webgl",kernelFunc:_i};var dst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=_i({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},T3={kernelName:Pi,backendName:"webgl",kernelFunc:dst};function hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Yw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var _3={kernelName:Oa,backendName:"webgl",kernelFunc:hst};var gst=`
  int r = int(a.r) & int(b.r);
  int g = int(a.g) & int(b.g);
  int rb = int(a.b) & int(b.b);
  int ra = int(a.a) & int(b.a);
  return vec4(r, g, rb, ra);
`,xst=`
  return float(int(a.r) & int(b.r));
`;function yst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=L().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=LL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new Jn(gst,n.shape,o.shape,!1):a=new On(xst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var E3={kernelName:Pa,backendName:"webgl",kernelFunc:yst};function bst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var A3={kernelName:Ql,backendName:"webgl",kernelFunc:bst};var wst="return float(a != b);",A1=ue({opSnippet:wst,cpuKernelImpl:sz,dtype:"bool"}),D3={kernelName:el,backendName:"webgl",kernelFunc:A1};function Ul(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var $3={kernelName:Yp,backendName:"webgl",kernelFunc:Ul};var Ist="return float(int(x));";function R3(r,t){let e=new Br(r.shape,Ist),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function D1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=D1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Pn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Ul({inputs:{input:o},backend:e}),a=D1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=zL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return R3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=A1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var F3={kernelName:xo,backendName:"webgl",kernelFunc:D1};var O3="return ceil(x);",Cst=It({opSnippet:O3,packedOpSnippet:O3,cpuKernelImpl:BL}),P3={kernelName:rs,backendName:"webgl",kernelFunc:Cst};var yI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
 
      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }
 
        setOutput(clamp(value, minVal, maxVal));
      }
    `}};var bI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();
 
        if (any(isnan(value))) {
          setOutput(value);
          return;
        }
 
        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function vst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new bI(o.shape):a=new yI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var M3={kernelName:yo,backendName:"webgl",kernelFunc:vst};var wI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);
 
        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `}};function L3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Sst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new wI(n.shape),i=[L3(n,o.complexTensorInfos.real),L3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var z3={kernelName:tu,backendName:"webgl",kernelFunc:Sst};var II=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i<e.length;i++)e[i]=e[i-1]+t[i][1];let n=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<e.length;i++){let a=e[i-1];n.push(`else if (yC < ${e[i]}) setOutput(getT${i}(yR, yC-${a}));`)}let o=e.length,s=e[e.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;
 
        ${n.join(`
        `)}
      }
    `}};var vI=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=zt(o),i=er("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h<u.length;h++)u[h]=u[h-1]+t[h][e];let l=a[e],c=a.slice(-2),p=a.join(),m=`if (${l} < ${u[0]}) {
        return getChannel(
            getT0(${p}), vec2(${c.join()}));
        }`;for(let h=1;h<u.length;h++){let g=u[h-1];m+=`
        if (${l} < ${u[h]}  && ${l} >= ${u[h-1]}) {
          return getChannel(
            getT${h}(${CI(a,l,g)}),
            vec2(${CI(c,l,g)}));
        }`}let f=u.length,d=u[u.length-1];m+=`
        return getChannel(
          getT${f}(${CI(a,l,d)}),
          vec2(${CI(c,l,d)}));`,this.userCode=`
      float getValue(${a.map(h=>"int "+h)}) {
        ${m}
      }
 
      void main() {
        ${s} coords = getOutputCoords();
        vec4 result = vec4(getValue(${i}), 0., 0., 0.);
 
        ${i[o-1]} = ${i[o-1]} + 1;
        if (${i[o-1]} < ${n[o-1]}) {
          result.g = getValue(${i});
        }
 
        ${i[o-2]} = ${i[o-2]} + 1;
        if (${i[o-2]} < ${n[o-2]}) {
          result.a = getValue(${i});
        }
 
        ${i[o-1]} = ${i[o-1]} - 1;
        if (${i[o-2]} < ${n[o-2]} &&
            ${i[o-1]} < ${n[o-1]}) {
          result.b = getValue(${i});
        }
        setOutput(result);
      }
    `}};function CI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function Sp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var B3={kernelName:qp,backendName:"webgl",kernelFunc:Sp};function zd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Ul({inputs:{input:b},backend:e})),d=r.map(b=>Sp({inputs:{input:b},backend:e})),h=zd(f,t,e),g=zd(d,t,e),x=Pn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=VL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new Br(r[0].shape,Na):new Fn(r[0].shape,Na);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;h<s.length;h+=a){let g=s.slice(h,h+a);f.push(zd(g,t,e))}let d=zd(f,t,e);for(let h of f)e.disposeIntermediateTensorInfo(h);return d}if(i){let f=new vI(s.map(d=>d.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Nst(s,t,e),c=new II(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Nst(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function $1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):zd(u,s,e)}var V3={kernelName:Mi,backendName:"webgl",kernelFunc:$1};var Bd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?I=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:I=`
          float activation(float x) {
            ${n}
          }
        `,N="result = activation(result);");let E=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${I}
 
      const ivec2 strides = ivec2(${u}, ${l});
      const ivec2 pads = ivec2(${i}, ${a});
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${w}];
 
        ivec2 xRCCorner =
            ivec2(coords[${x}], coords[${b}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;
 
        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${m}; wR++) {
          int xR = xRCorner + wR * ${c};
 
          if (xR < 0 || xR >= ${t.inHeight}) {
            continue;
          }
 
          for (int wC = 0; wC < ${f}; wC++) {
            int xC = xCCorner + wC * ${p};
 
            if (xC < 0 || xC >= ${t.inWidth}) {
              continue;
            }
 
            for (int d1 = 0; d1 < ${d}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );
 
              if (${g}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }
 
            if (${h===1}) {
 
              if (${g}) {
                dotProd +=
                    getX(batch, xR, xC, ${d}) *
                    getW(wR, wC, ${d}, d2);
              } else {
                dotProd +=
                    getX(batch, ${d}, xR, xC) *
                    getW(wR, wC, ${d}, d2);
              }
 
            } else if (${h===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${d}, d2),
                getW(wR, wC, ${d} + 1, d2)
              );
 
              if (${g}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${d}),
                  getX(batch, xR, xC, ${d} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${d}, xR, xC),
                  getX(batch, ${d} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
 
            } else if (${h===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${d}, d2),
                getW(wR, wC, ${d} + 1, d2),
                getW(wR, wC, ${d} + 2, d2)
              );
 
              if (${g}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${d}),
                  getX(batch, xR, xC, ${d} + 1),
                  getX(batch, xR, xC, ${d} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${d}, xR, xC),
                  getX(batch, ${d} + 1, xR, xC),
                  getX(batch, ${d} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
 
            }
          }
        }
 
        float result = dotProd;
        ${E}
        ${N}
        setOutput(result);
      }
    `}},SI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${s}, ${i}, ${a});
      const ivec3 pads = ivec3(${e}, ${n}, ${o});
 
      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;
 
        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;
 
        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${p}; wF++) {
          int xF = xFCorner + wF * ${u};
 
          if (xF < 0 || xF >= ${t.inDepth}) {
            continue;
          }
 
          for (int wR = 0; wR < ${m}; wR++) {
            int xR = xRCorner + wR * ${l};
 
            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }
 
            for (int wC = 0; wC < ${f}; wC++) {
              int xC = xCCorner + wC * ${c};
 
              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }
 
              for (int d1 = 0; d1 < ${d}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );
 
                dotProd += dot(xValues, wValues);
              }
 
              if (${h===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${d}) *
                  getW(wF, wR, wC, ${d}, d2);
              } else if (${h===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${d}),
                  getX(batch, xF, xR, xC, ${d} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${d}, d2),
                  getW(wF, wR, wC, ${d} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${h===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${d}),
                  getX(batch, xF, xR, xC, ${d} + 1),
                  getX(batch, xF, xR, xC, ${d} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${d}, d2),
                  getW(wF, wR, wC, ${d} + 1, d2),
                  getW(wF, wR, wC, ${d} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)m+=`
           vec4 xTexelC${g*2};
           int xTexelC${g*2}Ready;
           vec4 xTexelC${g*2+1};
           int xTexelC${g*2+1}Ready;
           vec4 xC${g};`;m+=`
     for (int r = 0; r < ${l}; r++) {
      for (int d1 = 0; d1 < ${t.inChannels}; d1 += 2) {
       `;for(let g=0;g<c;g++)m+=`
           xTexelC${g*2} = vec4(0.0);
           xTexelC${g*2}Ready = 0;
           xTexelC${g*2+1} = vec4(0.0);
           xTexelC${g*2+1}Ready = 0;
           xC${g} = vec4(0.0);`;m+=`
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`
           xC = xCCorner + ${x*u};
           `,a===1){if(x<c&&(i%2===1?(m+=`
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }
               `,u===1&&x>0?m+=`
                 xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);
                 `:m+=`
                   xCOffset = xC + 1 - 2;
 
                   if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);
 
                     // Need to manually clear unused channels in case
                     // we're reading from recycled texture.
                     if (xCOffset + 1 >= inDims[1]) {
                       previous.zw = vec2(0.0);
                     }
 
                     xC${x} = vec4(previous.zw, xTexelC${x}.xy);
                   } else {
                     xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);
                   }
                   `):m+=`
                 if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }
 
                 xC${x} = xTexelC${x};
                 `,x+1<c)){let b=i%2===0?y.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(m+=`
                   xCOffset = xC + imod(pads[1], 2) + ${b};
 
                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
                     xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
 
                     // Need to manually clear unused channels in case
                     // we're reading from recycled texture.
                     if (xCOffset + 1 >= inDims[1]) {
                       xTexelC${x+1}.zw = vec2(0.0);
                     }
                     xTexelC${x+1}Ready = 1;
                   }
                   `,u>1?m+=`
                     xCOffset -= 2;
                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
                      previous = getX(batch, xR, xCOffset, d1);
                      xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);
                     } else {
                      xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);
                     }
                     `:m+=`
                     xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);
                     `):b===1?m+=`
                     xC${x+1} = xTexelC${x};
                     `:m+=`
                     xCOffset = xC + ${b};
 
                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
                       xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
                       if (xCOffset + 1 >= inDims[1]) {
                         xTexelC${x+1}.zw = vec2(0.0);
                       }
                       xTexelC${x+1}Ready = 1;
                     }
 
                     xC${x+1} = xTexelC${x+1};
                     `}}else x<c&&(i%2===1?(m+=`
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = getX(batch, xR, xCOffset, d1);
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }
 
                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {
                   xTexelC${x+1} = getX(batch, xR, xC + 1, d1);
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xC + 2 >= inDims[1]) {
                     xTexelC${x+1}.zw = vec2(0.0);
                   }
                   xTexelC${x+1}Ready = 1;
                 }
 
                 xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
               `,x+1<c&&(m+=`
                   final = vec4(0.0);
                   xCOffset = xC + 1 + strides[1];
                   if(xCOffset >= 0 && xCOffset < inDims[1]) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
                   xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);
                 `)):(m+=`
                 if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }
 
                 xCOffset = xC + strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
                   xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${x+1}.zw = vec2(0.);
                   }
                   xTexelC${x+1}Ready = 1;
                 }
 
                 xC${x} = vec4(
                   xTexelC${x}.xy, xTexelC${x+1}.xy);
               `,x+1<c&&(m+=`
                   xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
                 `)));x<c&&(m+=`
             wTexel = getW(r, ${x}, d1, d2);
             dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${t.inChannels}) {
               dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `,x+1<c&&(m+=`
               wTexel = getW(r, ${x+1}, d1, d2);
               dotProd += xC${x+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${t.inChannels}) {
                 dotProd += xC${x+1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `))}m+=`
     }
   `,m+=`
     }
   `,m+=`
     }
   `;let f="",d="";n&&(o?f=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:s?f=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
         }`:f=`vec4 activation(vec4 x) {
           ${n}
         }`,d="result = activation(result);");let h=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${f}
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
         vec4 dotProd = vec4(0.000000000000001);
 
         ${m}
 
         vec4 result = dotProd - vec4(0.000000000000001);
         ${h}
         ${d}
         setOutput(result);
       }
     `}};var NI=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let{dataFormat:n}=e,o=We(),s=n==="channelsLast",i=s?1:2,a=s?2:3,u=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${t[2]} && pos < ${t[1]}) {`,l="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)l+=`
          blockIndex = rc.z + ${p};
          pos = rc.y + ${c};
 
          ${u}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
 
            if(d0 < inputShape[${i}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);
 
              if(d1 < inputShape[${a}] && d1 >= 0) {
 
                ch = imod(pos, inChannels);
 
                if (${s}) {
                  innerDims = vec2(d1, ch);
                  result[${c*2+p}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${c*2+p}] = getChannel(
                    getA(rc.x, ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec3 rc = getOutputCoords();
 
        vec4 result = vec4(0);
 
        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;
 
        ${l}
 
        ${o.output} = result;
      }
    `}};function kI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function TI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=kI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=kI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>_1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Ju(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=vp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=vp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function _I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=kI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=kI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new NI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Wl(a,!0):null,q=new Ld(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,"float32"),X=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return X}function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=TI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new Vd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=_I({x:o,filter:s,convInfo:m,backend:e});else{let h=new Bd(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var G3={kernelName:ns,backendName:"webgl",kernelFunc:kst};var EI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;
 
        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
 
        for (int b = 0; b < ${t.batchSize}; b++) {
          for (int yR = 0; yR < ${t.outHeight}; yR++) {
            int xR = wR + yR * ${e} - ${o};
 
            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }
 
            for (int yC = 0; yC < ${t.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${s};
 
              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }
 
              ${i?`float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);
              float xValue = getX(b, d1, xR, xC);
              dotProd += (xValue * dyValue);`}
            }
          }
        }
        setOutput(dotProd);
      }
    `}},AI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`
      const ivec2 pads = ivec2(${a}, ${u});
 
      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 < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;
 
          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
 
          int wRPerm = ${e} - 1 - wR;
 
          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${s}.0;
 
            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);
 
            int wCPerm = ${n} - 1 - wC;
 
            for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
 
              if (${i}) {
                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);
      }
    `}},DI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;
 
        float dotProd = 0.0;
 
        for (int b = 0; b < ${t.batchSize}; b++) {
          for (int yF = 0; yF < ${t.outDepth}; yF++) {
            int xF = wF + yF * ${e} - ${s};
 
            if (xF < 0 || xF >= ${t.inDepth}) {
              continue;
            }
 
            for (int yR = 0; yR < ${t.outHeight}; yR++) {
              int xR = wR + yR * ${n} - ${i};
 
              if (xR < 0 || xR >= ${t.inHeight}) {
                continue;
              }
 
              for (int yC = 0; yC < ${t.outWidth}; yC++) {
                int xC = wC + yC * ${o} - ${a};
 
                if (xC < 0 || xC >= ${t.inWidth}) {
                  continue;
                }
 
                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},$I=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${u}, ${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 < ${e}; wF++) {
          float dyF = float(dyFCorner + wF) / ${s}.0;
 
          if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);
 
          int wFPerm = ${e} - 1 - wF;
 
          for (int wR = 0; wR < ${n}; wR++) {
            float dyR = float(dyRCorner + wR) / ${i}.0;
 
            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);
 
            int wRPerm = ${n} - 1 - wR;
 
            for (int wC = 0; wC < ${o}; wC++) {
              float dyC = float(dyCCorner + wC) / ${a}.0;
 
              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);
 
              int wCPerm = ${o} - 1 - wC;
 
              for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new EI(m);return e.runWebGLProgram(f,[o,s],"float32")}var W3={kernelName:Bp,backendName:"webgl",kernelFunc:Tst};var RI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${s});
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
 
        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;
 
        vec4 result = vec4(0.);
        for (int wR = 0; wR < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / strides[0];
          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
          int wRPerm = ${e} - 1 - wR;
 
          for (int wC = 0; wC < ${n}; wC++) {
            int wCPerm = ${n} - 1 - wC;
 
            float dyC = float(dyCCorner + wC) / strides[1];
            bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)
              && (fract(dyC) == 0.0);
            int idyC = int(dyC);
 
            float dyC2 = float(dyCCorner + wC + 1) / strides[1];
            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)
              && (fract(dyC2) == 0.0);
            int idyC2 = int(dyC2);
 
            if (idyCVal && idyCVal2) {
              for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
                  dySample : getDy(batch, idyR, idyC2, d2);
 
                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
 
                dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample2.xy : dySample2.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal) {
              for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal2) {
              for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC2, d2);
                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            }
          }
        }
        setOutput(result);
      }
    `}};function _st(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new RI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new AI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var U3={kernelName:os,backendName:"webgl",kernelFunc:_st};function Est(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new SI(l);return e.runWebGLProgram(c,[o,s],"float32")}var H3={kernelName:ss,backendName:"webgl",kernelFunc:Est};function Ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new DI(l);return e.runWebGLProgram(c,[o,s],"float32")}var q3={kernelName:Ma,backendName:"webgl",kernelFunc:Ast};function Dst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new $I(l);return e.runWebGLProgram(c,[o,s],"float32")}var K3={kernelName:La,backendName:"webgl",kernelFunc:Dst};var $st=Vo+`
  return cos(x);
`,Rst=`
  vec4 result = cos(x);
  bvec4 isNaN = isnan(x);
  ${Qn}
  return result;
`,Fst=It({opSnippet:$st,packedOpSnippet:Rst}),j3={kernelName:is,backendName:"webgl",kernelFunc:Fst};var Ost=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,Pst=It({opSnippet:Ost}),X3={kernelName:as,backendName:"webgl",kernelFunc:Pst};var FI=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
      const float height_ratio = float(${g});
      const float width_ratio = float(${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 >= ${i}) {
          return;
        }
 
        float height_scale = ${x};
        float width_scale = ${I};
 
        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${d} ) {
          setOutput(float(${s}));
          return;
        }
        float in_x = ${N};
        if( in_x < 0.0 || in_x > ${h} ) {
          setOutput(float(${s}));
          return;
        }
 
        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${f} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
 
          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
 
          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
 
          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}};var Mst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new FI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},Y3={kernelName:Ba,backendName:"webgl",kernelFunc:Mst};var Np;(function(r){r.Prod="*",r.Sum="+"})(Np||(Np={}));var hg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===Np.Prod?"1.0":"0.0",a=n?i:`getX(${Z3(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${zt(s)} coords = getOutputCoords();
        int end = ${J3(s,"coords",this.op)};
        float val = ${a};
        int pow2 = int(pow(2.0, index));
        if (${l}) {
          int idx = ${c};
          ${J3(s,"coords",this.op)} = idx;
          val ${this.op}= getX(${Z3(s,"coords",this.op)});
        }
        setOutput(val);
      }
    `}};function Z3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function J3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function OI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new hg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new hg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(Np.Prod,o,e,s,i,a)}var Q3={kernelName:za,backendName:"webgl",kernelFunc:Lst};function zst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(Np.Sum,o,e,s,i,a)}var tB={kernelName:ls,backendName:"webgl",kernelFunc:zst};function Bst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Yw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=ML(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var eB={kernelName:eu,backendName:"webgl",kernelFunc:Bst};var PI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};
 
      int in_h = h / ${e};
      int offset_h = imod(h, ${e});
      int in_w = w / ${e};
      int offset_w = imod(w, ${e});
      int offset_d = (offset_h * ${e} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;
 
      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Vst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new PI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var rB={kernelName:Va,backendName:"webgl",kernelFunc:Vst};var Gd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?l=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:l=`
          float activation(float x) {
            ${n}
          }
        `,c="result = activation(result);");let p=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${l}
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${u};
        int q = d2 - d1 * ${u};
 
        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 < ${i}; wR++) {
          int xR = xRCorner + wR * dilations[0];
 
          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }
 
          for (int wC = 0; wC < ${a}; wC++) {
            int xC = xCCorner + wC * dilations[1];
 
            if (xC < 0 || xC >= inDims[1]) {
              continue;
            }
 
            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }
 
        float result = dotProd;
        ${p}
        ${c}
        setOutput(result);
      }
    `}};var Wd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<p;x++)f+=`
          vec4 xTexelC${x*2};
          int xTexelC${x*2}Ready;
          vec4 xTexelC${x*2+1};
          int xTexelC${x*2+1}Ready;
          vec4 xC${x};`;f+=`
    for (int r = 0; r < ${c}; r++) {
      `;for(let x=0;x<p;x++)f+=`
          xTexelC${x*2} = vec4(0.0);
          xTexelC${x*2}Ready = 0;
          xTexelC${x*2+1} = vec4(0.0);
          xTexelC${x*2+1}Ready = 0;
          xC${x} = vec4(0.0);`;f+=`
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`
          xC = xCCorner + ${b*l};
          `,u===1){if(b<p&&(a%2===1?(f+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xCOffset, d1);
 
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
              `,l===1&&b>0?f+=`
                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
                `:f+=`
                  xCOffset = xC + 1 - 2;
 
                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
                    previous = getX(batch, xR, xCOffset, d1);
 
                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      previous.zw = vec2(0.0);
                    }
 
                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);
                  } else {
                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
                  }
                  `):f+=`
                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
 
                xC${b} = xTexelC${b};
                `,b+1<p)){let w=a%2===0?y.nearestLargerEven(l):l;l%2===0&&a%2===1||l%2!==0&&a%2!==1?(f+=`
                  xCOffset = xC + imod(pads[1], 2) + ${w};
 
                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
 
                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${b+1}.zw = vec2(0.0);
                    }
                    xTexelC${b+1}Ready = 1;
                  }
                  `,l>1?f+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);
                     xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);
                    } else {
                     xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);
                    }
                    `:f+=`
                    xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
                    `):w===1?f+=`
                    xC${b+1} = xTexelC${b};
                    `:f+=`
                    xCOffset = xC + ${w};
 
                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${b+1}.zw = vec2(0.0);
                      }
                      xTexelC${b+1}Ready = 1;
                    }
 
                    xC${b+1} = xTexelC${b+1};
                    `}}else b<p&&(a%2===1?(f+=`
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
 
                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= inDims[1]) {
                    xTexelC${b+1}.zw = vec2(0.0);
                  }
                  xTexelC${b+1}Ready = 1;
                }
 
                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
              `,b+1<p&&(f+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
                `)):(f+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
 
                xCOffset = xC + strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b+1}.zw = vec2(0.);
                  }
                  xTexelC${b+1}Ready = 1;
                }
 
                xC${b} = vec4(
                  xTexelC${b}.xy, xTexelC${b+1}.xy);
              `,b+1<p&&(f+=`
                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
                `)));b<p&&(f+=`
            wTexel = getW(r, ${b}, d1, q);
            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
          `,b+1<p&&(f+=`
              wTexel = getW(r, ${b+1}, d1, q);
              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
            `))}f+=`
    }
  `,f+=`
      }
    `;let d="",h="";n&&(o?d=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?d=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:d=`vec4 activation(vec4 x) {
          ${n}
        }`,h="result = activation(result);");let g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${d}
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${i};
        int q = d2 - d1 * ${i};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;
 
        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);
 
        ${f}
 
        vec4 result = dotProd - vec4(0.000000000000001);
        ${g}
        ${h}
        setOutput(result);
      }
    `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Wd(p):m=new Gd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var nB={kernelName:us,backendName:"webgl",kernelFunc:Gst};var MI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${i} + dm;
 
        float dotProd = 0.0;
 
        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${t.batchSize}; b++) {
          for (int yR = 0; yR < ${t.outHeight}; yR++) {
            int xR = wR + yR * ${e} - ${o};
 
            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }
 
            for (int yC = 0; yC < ${t.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${s};
 
              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }
 
              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}},LI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${a});
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;
 
        float dotProd = 0.0;
 
        for (int wR = 0; wR < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;
 
          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
 
          int wRPerm = ${e} - 1 - wR;
 
          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${s}.0;
 
            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);
 
            int wCPerm = ${n} - 1 - wC;
 
            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${u}; dm++) {
              int d2 = d1 * ${u} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Wst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new MI(p);return e.runWebGLProgram(m,[o,s],"float32")}var oB={kernelName:Vp,backendName:"webgl",kernelFunc:Wst};function Ust(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new LI(p);return e.runWebGLProgram(m,[o,s],"float32")}var sB={kernelName:Gp,backendName:"webgl",kernelFunc:Ust};var zI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function Hst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new zI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var iB={kernelName:ru,backendName:"webgl",kernelFunc:Hst};var BI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`
      const ivec2 strides = ivec2(${s}, ${i});
      const ivec2 pads = ivec2(${p}, ${m});
      const float neg_infinity = -3.4e38;
 
      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;
 
        float curVal = neg_infinity;
        for (int h = 0; h < ${a}; h++) {
          int hIn = hBeg + h * ${l};
 
          if (hIn >= 0 && hIn < ${e}) {
            for (int w = 0; w < ${u}; w++) {
              int wIn = wBeg + w * ${c};
 
              if (wIn >= 0 && wIn < ${n}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);
 
                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }
 
        float result = curVal;
        setOutput(result);
      }
    `}};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new BI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var aB={kernelName:cs,backendName:"webgl",kernelFunc:qst};function Kst(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(x)?w=s[g]:(w=Pe({inputs:{x:s[g]},backend:e,attrs:{perm:x}}),d.push(w));let I=w.shape.slice();for(let N=0;N<b.length;++N)I.splice(b[N],0,1);y.arraysEqual(w.shape,I)||(w=rt({inputs:{x:w},backend:e,attrs:{shape:I}}),d.push(w)),m===null?m=w:(m=fg({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=Cp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var lB={kernelName:Wp,backendName:"webgl",kernelFunc:Kst};var jst="return (x >= 0.0) ? x : (exp(x) - 1.0);",Xst=`
  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;
`,Yst=It({opSnippet:jst,packedOpSnippet:Xst}),uB={kernelName:ms,backendName:"webgl",kernelFunc:Yst};var Zst="return (b >= 0.0) ? a : a * (b + 1.0);",Jst=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Qst=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jn(Jst,n.shape,o.shape):new On(Zst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},cB={kernelName:Ga,backendName:"webgl",kernelFunc:Qst};var tit=`
  return vec4(equal(a, b));
`,eit="return float(a == b);",rit=ue({opSnippet:eit,packedOpSnippet:tit,dtype:"bool",cpuKernelImpl:GL}),pB={kernelName:Wa,backendName:"webgl",kernelFunc:rit};var nit=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${S.ERF_P};
  float a1 = ${S.ERF_A1};
  float a2 = ${S.ERF_A2};
  float a3 = ${S.ERF_A3};
  float a4 = ${S.ERF_A4};
  float a5 = ${S.ERF_A5};
 
  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,oit=It({opSnippet:nit}),mB={kernelName:fs,backendName:"webgl",kernelFunc:oit};var sit=Vo+`
  return exp(x);
`,iit=`
  vec4 result = exp(x);
  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;
`,R1=It({opSnippet:sit,packedOpSnippet:iit,cpuKernelImpl:WL,dtype:"float32"}),fB={kernelName:ds,backendName:"webgl",kernelFunc:R1};function VI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var dB={kernelName:Li,backendName:"webgl",kernelFunc:VI};var hB="return exp(x) - 1.0;",ait=It({opSnippet:hB,packedOpSnippet:hB,cpuKernelImpl:UL}),gB={kernelName:hs,backendName:"webgl",kernelFunc:ait};var gg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
      const float exponentMultiplier = ${s};
 
      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${a}
      }
 
      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${o});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;
 
        float result = 0.0;
 
        for (int i = 0; i < ${o}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);
 
          result +=
              unaryOpComplex(real, expR, imag, expI) / ${i};
        }
 
        return result;
      }
 
      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function GI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new gg("real",u,t),c=new gg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Pn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function lit(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!1,e)}var xB={kernelName:Up,backendName:"webgl",kernelFunc:lit};var WI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function Hl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new WI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var yB={kernelName:su,backendName:"webgl",kernelFunc:Hl};var UI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
 
          int coordX = ${e} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${e}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var bB={kernelName:Ua,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new UI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var wB="return floor(x);",uit=It({opSnippet:wB,packedOpSnippet:wB,cpuKernelImpl:HL}),IB={kernelName:gs,backendName:"webgl",kernelFunc:uit};var cit=`
  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;
  }
`,pit=`
  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);
`,mit=ue({opSnippet:cit,packedOpSnippet:pit,dtype:"int32"}),CB={kernelName:xs,backendName:"webgl",kernelFunc:mit};var HI=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
 
        vec4 values = ${e.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }
 
        setOutput(floor(value * 255.0 + 0.5));
      }
    `}};var qI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
 
        vec4 result = vec4(0.);
 
        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;
 
            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${o}.0, ${n}.0);
            vec4 values = ${e.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }
 
            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }
 
        ${e.output} = result;
      }
    `}};var vB={kernelName:oh,backendName:"webgl",kernelFunc:fit},Ud,F1=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function fit(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ud==null||h!==F1)&&(F1=h,Ud=document.createElement("canvas").getContext("2d",{willReadFrequently:F1})),Ud.canvas.width=u,Ud.canvas.height=l,Ud.drawImage(o,0,0,u,l),o=Ud.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Jr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool("WEBGL_PACK")?new qI(p):new HI(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function dit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f==="leakyrelu",E=()=>{let D=[o,s],F=(P,V)=>{if(V==="NCHW"&&P.shape.length===1&&P.shape[0]!==1){let G=rt({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let P=e.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));D.push(P),b.push(P)}return D};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=TI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let D=f?Wl(f,!0):null,F=new Vd(g,w,D,I,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,"float32",P)}else if(L().getBool("WEBGL_CONV_IM2COL"))x=_I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Wl(f,!1):null,F=new Bd(g,w,D,I,N),P=E();x=e.runWebGLProgram(F,P,"float32")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var SB={kernelName:Ji,backendName:"webgl",kernelFunc:dit};function hit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Wl(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m==="leakyrelu";if(I&&w.push(i),N&&w.push(a),E){let P=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(P),d.push(P)}let A;x?A=new Wd(g,I,b,N,E):A=new Gd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,"float32",D);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var NB={kernelName:Qi,backendName:"webgl",kernelFunc:hit};var KI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=["x","indices"],this.outputShape=n;let s=zt(n.length),i=`
    int index;`;for(let a=0;a<this.sliceDim;a++)i+=`
          index = round(getIndices(coords[0], ${a}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[a]};
          flattenIndex += index * ${this.strides[a]};`;this.userCode=`
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;
 
          ${i}
 
          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `}};function git(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=qL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new KI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var kB={kernelName:Ha,backendName:"webgl",kernelFunc:git};var jI=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=xit(t,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${o}));
      }
    `}};function xit(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("index"):n.push(`${e[o]}`);return n.join()}function O1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0];if(L().get("DEBUG")){let b=e.readSync(s.dataId),w=o.shape[u];for(let I=0;I<b.length;++I){let N=b[I];y.assert(N<=w-1&&N>=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),I=KL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new jI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var TB={kernelName:zi,backendName:"webgl",kernelFunc:O1};var yit="return float(a > b);",bit=`
  return vec4(greaterThan(a, b));
`,wit=ue({opSnippet:yit,packedOpSnippet:bit,cpuKernelImpl:jL,dtype:"bool"}),_B={kernelName:qa,backendName:"webgl",kernelFunc:wit};var Iit="return float(a >= b);",Cit=`
  return vec4(greaterThanEqual(a, b));
`,vit=ue({opSnippet:Iit,packedOpSnippet:Cit,dtype:"bool",cpuKernelImpl:XL}),EB={kernelName:bs,backendName:"webgl",kernelFunc:vit};function Sit(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!0,e)}var AB={kernelName:Hp,backendName:"webgl",kernelFunc:Sit};var Nit="return float(!isnan(x) && !isinf(x));",kit=It({opSnippet:Nit,dtype:"bool"}),DB={kernelName:ws,backendName:"webgl",kernelFunc:kit};var Tit="return float(isinf(x));",_it=It({opSnippet:Tit,dtype:"bool"}),$B={kernelName:Is,backendName:"webgl",kernelFunc:_it};var Eit="return float(isnan(x));",Ait=It({opSnippet:Eit,dtype:"bool"}),RB={kernelName:Cs,backendName:"webgl",kernelFunc:Ait};var Dit="return float(a < b);",$it=`
  return vec4(lessThan(a, b));
`,Rit=ue({opSnippet:Dit,packedOpSnippet:$it,cpuKernelImpl:YL,dtype:"bool"}),FB={kernelName:Ka,backendName:"webgl",kernelFunc:Rit};var Fit="return float(a <= b);",Oit=`
  return vec4(lessThanEqual(a, b));
`,Pit=ue({opSnippet:Fit,packedOpSnippet:Oit,cpuKernelImpl:ZL,dtype:"bool"}),OB={kernelName:ja,backendName:"webgl",kernelFunc:Pit};function Mit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=JL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var PB={kernelName:Xa,backendName:"webgl",kernelFunc:Mit};var Lit=Vo+`
  return x < 0.0 ? 0./0. : log(x);
`,zit=`
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`,Bit=It({opSnippet:Lit,packedOpSnippet:zit,cpuKernelImpl:QL}),MB={kernelName:Ss,backendName:"webgl",kernelFunc:Bit};var Vit=Vo+`
  return log(1.0 + x);
`,Git=It({opSnippet:Vit}),LB={kernelName:Ns,backendName:"webgl",kernelFunc:Git};var Wit="return float(a >= 1.0 && b >= 1.0);",Uit=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Hit=ue({opSnippet:Wit,packedOpSnippet:Uit,dtype:"bool"}),zB={kernelName:Ya,backendName:"webgl",kernelFunc:Hit};var qit="return float(!(x >= 1.0));",Kit=It({opSnippet:qit}),BB={kernelName:Za,backendName:"webgl",kernelFunc:Kit};var jit="return float(a >= 1.0 || b >= 1.0);",Xit=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,Yit=ue({opSnippet:jit,packedOpSnippet:Xit,dtype:"bool"}),VB={kernelName:Ja,backendName:"webgl",kernelFunc:Yit};var XI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${i}; j <= ${i}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${a}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${u};
        setOutput(val);
      }
    `}};var YI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;
 
        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};
 
        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);
 
        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );
 
        int firstChannel = d - ${i};
        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 = - ${i}; j <= ${i}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));
 
          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 * ${u};
        setOutput(result);
      }
    `}};var Zit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new YI(o.shape,s,i,a,u):new XI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},GB={kernelName:ks,backendName:"webgl",kernelFunc:Zit};var ZI=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
 
        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${e})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${e} + 1)));
 
          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};
 
          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }
 
          norm = float(${o}) * norm + float(${n});
 
          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${o})
                * float(${s})
                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}};var Jit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new ZI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},WB={kernelName:Qa,backendName:"webgl",kernelFunc:Jit};function UB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=to(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function P1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A<I.length;A++)I[A]=o.shape[c[A]];let N=Ip(w,o.shape,o.dtype,c,I);f=e.makeTensorInfo(I,o.dtype);let E=e.texData.get(f.dataId);E.values=N}else f=tc(o,c,e);l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("max",l,a);let[d,h]=S.computeOutAndReduceShapes(f.shape,l),g=d;i&&(g=S.expandShapeToKeepDim(d,u));let x;if(m){let w=e.texData.get(f.dataId).values,I=tz(w,y.sizeFromShape(h),g,o.dtype);x=e.makeTensorInfo(g,o.dtype);let N=e.texData.get(x.dataId);N.values=I}else x=UB(f,h,g,e);return p&&e.disposeIntermediateTensorInfo(f),x}var HB={kernelName:Ts,backendName:"webgl",kernelFunc:P1};var Qit=Md+`
  return max(a, b);
`,tat=`
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+Qn+`
  return result;
`,eat=ue({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:ez}),qB={kernelName:_s,backendName:"webgl",kernelFunc:eat};function rat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ni(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new Ti(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var KB={kernelName:Es,backendName:"webgl",kernelFunc:rat};function nat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new ec(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var jB={kernelName:Bi,backendName:"webgl",kernelFunc:nat};var JI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`
      const ivec2 pads = ivec2(${a}, ${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 < ${s};
          wR += ${o}) {
          float dyR = float(dyRCorner + wR) / ${e}.0;
 
          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
 
          for (int wC = 0; wC < ${i}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;
 
            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);
 
            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
 
            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${i} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},QI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${m}, ${f});
 
      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;
 
        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;
 
        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
 
        for (int wD = 0; wD < ${u};
           wD += ${s}) {
          float dyD = float(dyDCorner + wD) / ${e}.0;
 
          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);
 
          for (int wR = 0; wR < ${l};
              wR += ${i}) {
            float dyR = float(dyRCorner + wR) / ${n}.0;
 
            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);
 
            for (int wC = 0; wC < ${c};
                wC += ${a}) {
              float dyC = float(dyCCorner + wC) / ${o}.0;
 
              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);
 
              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${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);
      }
    `}};function oat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new ec(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new QI(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var XB={kernelName:au,backendName:"webgl",kernelFunc:oat};function sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ni([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ti(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new JI(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var YB={kernelName:iu,backendName:"webgl",kernelFunc:sat};function ZB(r,t,e,n){let o=new Ti(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Ti(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var JB={kernelName:lu,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=ZB(n,a,c,u);return[p,m]}};function QB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=to(a,"float32","mean",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var tV={kernelName:As,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let I=i.texData.get(d.dataId).values,N=new Array(a);for(let D=0;D<N.length;D++)N[D]=n.shape[c[D]];let E=Ip(I,n.shape,n.dtype,c,N);d=i.makeTensorInfo(N,n.dtype);let A=i.texData.get(d.dataId);A.values=E}else d=tc(n,c,i);f.push(d),l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("sum",l,a);let[h,g]=S.computeOutAndReduceShapes(d.shape,l),x=h;o&&(x=S.expandShapeToKeepDim(h,u));let b=QB(d,g,x,i);for(let w of f)i.disposeIntermediateTensorInfo(w);return b}};function iat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=to(h,h.dtype,"min",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var eV={kernelName:Ds,backendName:"webgl",kernelFunc:iat};var aat=Md+`
  return min(a, b);
`,lat=`
  vec4 result = vec4(min(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+Qn+`
  return result;
`,uat=ue({opSnippet:aat,packedOpSnippet:lat,cpuKernelImpl:rz}),rV={kernelName:$s,backendName:"webgl",kernelFunc:uat};var tC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[0]+t[p]+c[1]);let o=t.length,s=zt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=`
        int start = ${i};
        int end = ${a};
 
        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${l};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${l};
          }
          setOutput(getX(outC - start));
        }
      `;return}this.userCode=`
      ${s} start = ${s}(${i});
      ${s} end = ${s}(${a});
 
      void main() {
        ${s} outC = getOutputCoords();
        for (int i = 0; i < ${o}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${l};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
          }
        }
        ${s} coords = outC - start;
        setOutput(getX(${u}));
      }
    `}};var eC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
        ${s} source = rc;
        if (source < start) {
          source = start * 2 - source - ${m};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${m};
        }
        source -= start;
      `;f=`
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${u[o-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
      `}else{let d=`
        ${s} source = rc;
        ${s} lt = ${s}(lessThan(source, start));
        ${s} gte = ${s}(greaterThanEqual(source, end));
        ${s} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${m}) +
                gte * ((end - 1) * 2 - source + ${m});
        source -= start;
      `;f=`
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${u[o-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
        rc = outputLoc;
        ${u[o-2]} += 1;
        if(${u[o-2]} < ${this.outputShape[o-2]}) {
          ${d}
          result[2] = getChannel(getX(${l.join()}), ${p});
          ${u[o-1]} += 1;
          if(${c}) {
            ${d}
            result[3] = getChannel(getX(${l.join()}), ${p});
          }
        }
      `}this.userCode=`
      const ${s} start = ${s}(${i});
      const ${s} end = ${s}(${a});
 
      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${f}
        setOutput(result);
      }
    `}};var cat=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eC(n.shape,o,s):new tC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},nV={kernelName:Rs,backendName:"webgl",kernelFunc:cat};var pat=`if (b == 0.0) return NAN;
  return mod(a, b);`,mat=`
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  `+Qn+`
  return result;
`,fat=ue({opSnippet:pat,packedOpSnippet:mat}),oV={kernelName:Fs,backendName:"webgl",kernelFunc:fat};var rC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
 
        float r = random(seed);
        float cdf = 0.0;
 
        for (int i = 0; i < ${e-1}; i++) {
          cdf += getProbs(batch, i);
 
          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }
 
        // If no other event happened, last event happened.
        setOutput(float(${e-1}));
      }
    `}};var dat=`
if (a == b) {
  return 1.0;
};
return a / b;`,hat=`
  // 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;
`,M1=ue({opSnippet:dat,packedOpSnippet:hat,checkOutOfBounds:!0}),sV={kernelName:ps,backendName:"webgl",kernelFunc:M1};var iV="return a - b;",L1=ue({opSnippet:iV,packedOpSnippet:iV,supportsComplex:!0,cpuKernelImpl:vz}),aV={kernelName:si,backendName:"webgl",kernelFunc:L1};function z1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=P1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=L1({inputs:{a:o,b:l},backend:e}),p=R1({inputs:{x:c},backend:e}),m=Cp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=M1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var lV={kernelName:ni,backendName:"webgl",kernelFunc:z1};function gat(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:z1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new rC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var uV={kernelName:tl,backendName:"webgl",kernelFunc:gat};var xat=yr+`
  return -x;
`,yat=`
  vec4 result = -x;
  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;
`;function bat(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=oz(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fn(n.shape,yat):o=new Br(n.shape,xat),e.runWebGLProgram(o,[n],n.dtype)}var cV={kernelName:Vi,backendName:"webgl",kernelFunc:bat};var wat=jr.nonMaxSuppressionV3Impl;function Iat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=wat(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var pV={kernelName:rl,backendName:"webgl",kernelFunc:Iat};var Cat=jr.nonMaxSuppressionV4Impl;function vat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Cat(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var mV={kernelName:nl,backendName:"webgl",kernelFunc:vat};var Sat=jr.nonMaxSuppressionV5Impl;function Nat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Sat(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var fV={kernelName:ol,backendName:"webgl",kernelFunc:Nat};var nC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${o}), float(${n}),
                      float(index == coords.y)));
      }
    `}};var kat=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new nC(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},dV={kernelName:Ps,backendName:"webgl",kernelFunc:kat};function xg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Ul({inputs:{input:n},backend:e}),s=xg({inputs:{x:o},backend:e}),i=Sp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Pn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Hl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var hV={kernelName:Yi,backendName:"webgl",kernelFunc:xg};function gV(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Ul({inputs:{input:n},backend:e}),s=gV({inputs:{x:o},backend:e}),i=Sp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Pn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Hl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var xV={kernelName:Gi,backendName:"webgl",kernelFunc:gV};function Tat(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return VI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=VI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=$1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var yV={kernelName:Wi,backendName:"webgl",kernelFunc:Tat};var oC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
        int start = ${i};
        int end = ${a};
 
        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${s} start = ${s}(${i});
      ${s} end = ${s}(${a});
 
      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${u}));
        }
      }
    `}};var sC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;
       if(${c}) {
      `,o===1?"":`}
       rc = outputLoc;
       ${u[o-2]} += 1;
       if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${u[o-1]} += 1;
         if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
        ${m[h]}
        if (${f}) {
          result[${h}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${h}] = getChannel(getX(${l.join()}), ${p});
        }
      `;d+=o===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${i});
      const ${s} end = ${s}(${a});
 
      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}};var B1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Hl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sC(o.shape,s,i):new oC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},bV={kernelName:Ms,backendName:"webgl",kernelFunc:B1};var _at=`
  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);
`,Eat=`
  // 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;
 
  bvec4 isNaN1 = lessThan(a, vec4(0.0));
  bvec4 isNaN2 = lessThan(floor(b), b);
  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
  `+Qn+`
  return result;
`,Aat=ue({opSnippet:_at,packedOpSnippet:Eat}),wV={kernelName:Ls,backendName:"webgl",kernelFunc:Aat};function Dat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=iz(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=xc(o.dtype),w=to(x,b,"prod",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var IV={kernelName:Bs,backendName:"webgl",kernelFunc:Dat};function $at(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=az(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var CV={kernelName:Kp,backendName:"webgl",kernelFunc:$at};function Rat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=lz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var vV={kernelName:jp,backendName:"webgl",kernelFunc:Rat};function Fat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=uz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var SV={kernelName:Xp,backendName:"webgl",kernelFunc:Fat};var V1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=cz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},NV={kernelName:uu,backendName:"webgl",kernelFunc:V1};var Oat="return 1.0 / x;",Pat=It({opSnippet:Oat}),kV={kernelName:Vs,backendName:"webgl",kernelFunc:Pat};var Mat=yr+`
  return (x < 0.0) ? 0.0 : x;
`,Lat=`
  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;
`,zat=It({opSnippet:Mat,packedOpSnippet:Lat}),TV={kernelName:Gs,backendName:"webgl",kernelFunc:zat};var Bat=yr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Vat=`
  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;
`,Gat=It({opSnippet:Bat,packedOpSnippet:Vat}),_V={kernelName:Hs,backendName:"webgl",kernelFunc:Gat};var iC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/p[0]},
          ${c[1]/p[1]});
      const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
 
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;
 
        // Fractional source index.
        vec2 sourceFracIndexRC = ${m};
 
        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
 
        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
 
        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
 
        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;
 
        setOutput(newValue);
      }
    `}};var aC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/p[0]},
          ${c[1]/p[1]},
          ${c[1]/p[1]});
      const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
                                     ${u}.0);
 
      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }
 
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
 
        // Fractional source index.
        vec3 sourceFracIndexRC = ${m};
 
        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
 
        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${n-1};
 
        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
 
        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
 
        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
 
        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
 
        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
 
        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);
 
        setOutput(newValue);
      }
    `}};function Wat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aC(o.shape,u,l,s,i):new iC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var EV={kernelName:Us,backendName:"webgl",kernelFunc:Wat};var lC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];
 
        float accumulator = 0.0;
 
        const float heightScale = float(${c});
        const float widthScale = float(${p});
 
        const float invHeightScale = float(${m});
        const float invWidthScale = float(${f});
 
        const int winHeight = int(${d});
        const int winWidth = int(${h});
 
        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));
 
        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));
 
        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;
 
          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${i}) {
            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 >= ${a}) {
              continue;
            }
 
            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;
 
            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;
 
            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }
 
            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }
 
            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }
 
            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy
 
        setOutput(accumulator);
      }
    `}};function Uat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new lC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var AV={kernelName:il,backendName:"webgl",kernelFunc:Uat};var uC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/p[0]},
          ${c[1]/p[1]});
      const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
 
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;
 
        // Fractional source index.
        vec2 sourceFracIndexRC = ${f};
 
        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
 
        setOutput(newValue);
      }
    `}};var cC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/p[0]},
          ${c[1]/p[1]},
          ${c[1]/p[1]});
      const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
                                     ${u}.0);
 
      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }
 
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
 
        // Fractional source index.
        vec3 sourceFracIndexRC = ${f};
 
        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
 
        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${n-1};
 
        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
 
        setOutput(newValue);
      }
    `}};function Hat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cC(o.shape,u,l,s,i):new uC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var DV={kernelName:Ws,backendName:"webgl",kernelFunc:Hat};var pC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];
 
        float accumulator = 0.0;
 
        const float heightScale = float(${c});
        const float widthScale = float(${p});
 
        const float invHeightScale = float(${m});
        const float invWidthScale = float(${f});
 
        const int winHeight = int(${d});
        const int winWidth = int(${h});
 
        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));
 
        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));
 
        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;
 
          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${i}) {
            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 >= ${a}) {
              continue;
            }
 
            float sourceFracRow =
              float(${u[0]}) *
                (float(dyR) / float(${l[0]}));
 
            float sourceFracCol =
                float(${u[1]}) *
                  (float(dyC) / float(${l[1]}));
 
            int sourceNearestRow = int(min(
                float(int(${o}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));
 
            int sourceNearestCol = int(min(
                float(int(${s}) - 1),
                ${n} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));
 
            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy
 
        setOutput(accumulator);
      }
    `}};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new pC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var $V={kernelName:sl,backendName:"webgl",kernelFunc:qat};var mC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${t[0]} - coord - 1));
        }
      `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=zt(n);this.userCode=`
      void main() {
        ${i} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}};var fC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${t[0]} - rc - 1),
            ${t[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${t[0]} - (rc  + 1) - 1),
                ${t[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${a} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${u(o.slice())};
          if(${s}){
            result.g = ${l(o.slice())};
          }
          if(${i}) {
            result.b = ${c(o.slice())};
            if(${s}) {
              result.a = ${p(o.slice())};
            }
          }
          setOutput(result);
        }
    `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Kat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fC(o.shape,a):new mC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var RV={kernelName:qs,backendName:"webgl",kernelFunc:Kat};var dC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=`
        vec3 fill = vec3(${e.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${s}
          if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var FV={kernelName:hl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new dC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var jat=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,Xat=It({opSnippet:jat}),OV={kernelName:Ks,backendName:"webgl",kernelFunc:Xat};var Yat="return inversesqrt(x);",Zat=It({opSnippet:Yat,cpuKernelImpl:pz}),PV={kernelName:js,backendName:"webgl",kernelFunc:Zat};var rc=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=`
        ${l} strides = ${l}(${s});
 
        void main() {
          ${c} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${t}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${e}; j++) {
              int index = round(${m});
              flattenedIndex += index * ${x};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(${g}, sum, float(found)));
        }
      `}};var hC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=`
        ${l} strides = ${l}(${s});
 
        void main() {
          ${c} coords = getOutputCoords();
          vec4 sum = vec4(0.);
          vec4 found = vec4(0.);
          for (int i = 0; i < ${t}; i+=2) {
            ivec2 flattenedIndex = ivec2(0);
            for (int j = 0; j < ${e}; j+=2) {
              ivec4 index = round(${m});
              flattenedIndex += index.xz * ${x};
              if (j + 1 < ${e}) {
                flattenedIndex += index.yw * ${b};
              }
            }
            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
              vec4 updVals = ${d};
              if (flattenedIndex[0] == coords[0]) {
                sum.xy += updVals.xy;
                found.xy = vec2(1.);
              } else if (flattenedIndex[0] == coords[0] + 1) {
                sum.zw += updVals.xy;
                found.zw = vec2(1.);
              }
              if (flattenedIndex[1] == coords[0]) {
                sum.xy += updVals.zw;
                found.xy = vec2(1.);
              } else if (flattenedIndex[1] == coords[0] + 1) {
                sum.zw += updVals.zw;
                found.zw = vec2(1.);
              }
            }
          }
          setOutput(mix(${g}, sum, found));
        }
      `}};function Jat(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new hC(u,a,f.shape.length,d.shape.length,c,m):g=new rc(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var MV={kernelName:al,backendName:"webgl",kernelFunc:Jat};var gC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=`
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${a}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${u} value) {
             left = mid + 1;
           } else {
             right = mid;
           }
         }
         return right;
       }
 
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int valueIndex = coords[1];
 
         float value = getValues(batch, valueIndex);
 
         setOutput(float(findBound(batch, value)));
       }
     `}};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new gC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var LV={kernelName:ul,backendName:"webgl",kernelFunc:Qat};var xC=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c<e.length;c++)l.push(`${a[c]}`),c<t&&u.push(`${a[c]}`);o=u.join(),s=l.join()}let i=zt(n);this.userCode=`
      void main() {
        ${i} resRC = getOutputCoords();
        float cVal = getC(${o});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function tlt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new xC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var zV={kernelName:Hi,backendName:"webgl",kernelFunc:tlt};var elt=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${S.SELU_SCALEALPHA};
  float scale = ${S.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,rlt=It({opSnippet:elt}),BV={kernelName:Xs,backendName:"webgl",kernelFunc:rlt};var nlt=Vo+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,olt=`
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  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;
`,slt=It({opSnippet:nlt,packedOpSnippet:olt,cpuKernelImpl:fz}),VV={kernelName:Qs,backendName:"webgl",kernelFunc:slt};var ilt=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,alt=It({opSnippet:ilt}),GV={kernelName:Js,backendName:"webgl",kernelFunc:alt};var llt=Vo+`
  return sin(x);
`,ult=`
  vec4 result = sin(x);
  bvec4 isNaN = isnan(x);
  ${Qn}
  return result;
`,clt=It({opSnippet:llt,packedOpSnippet:ult}),WV={kernelName:Ys,backendName:"webgl",kernelFunc:clt};var plt=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,mlt=It({opSnippet:plt}),UV={kernelName:Zs,backendName:"webgl",kernelFunc:mlt};var flt=`
  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;
`,dlt=It({opSnippet:flt}),HV={kernelName:ti,backendName:"webgl",kernelFunc:dlt};var hlt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;x<o.shape.length;++x)u.push([0,0]);let l=[],c=B1({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,s,a,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,a,!1),d=rt({inputs:{x:c},backend:e,attrs:{shape:p}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:m}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:f}});return l.push(c),l.push(d),l.push(h),l.forEach(x=>e.disposeIntermediateTensorInfo(x)),g},qV={kernelName:Ki,backendName:"webgl",kernelFunc:hlt};function glt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=hz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var KV={kernelName:cu,backendName:"webgl",kernelFunc:glt};function xlt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=gz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var jV={kernelName:cl,backendName:"webgl",kernelFunc:xlt};function ylt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var XV={kernelName:pu,backendName:"webgl",kernelFunc:ylt};function blt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var YV={kernelName:mu,backendName:"webgl",kernelFunc:blt};function wlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=mz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new rc(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var ZV={kernelName:pl,backendName:"webgl",kernelFunc:wlt};function Ilt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=_i({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var JV={kernelName:ji,backendName:"webgl",kernelFunc:Ilt};var QV="return sqrt(x);",Clt=It({opSnippet:QV,packedOpSnippet:QV,cpuKernelImpl:xz}),tG={kernelName:ei,backendName:"webgl",kernelFunc:Clt};var vlt="return x * x;",Slt=It({opSnippet:vlt}),eG={kernelName:fu,backendName:"webgl",kernelFunc:Slt};var rG="return (a - b) * (a - b);",Nlt=ue({opSnippet:rG,packedOpSnippet:rG}),nG={kernelName:oi,backendName:"webgl",kernelFunc:Nlt};function klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=yz(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var oG={kernelName:cc,backendName:"webgl",kernelFunc:klt};function Tlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Br(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var sG={kernelName:wo,backendName:"webgl",kernelFunc:Tlt};var yC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${t});
      ${s} strides = ${s}(${e});
 
      void main() {
        ${i} coords = getOutputCoords();
        setOutput(getX(${a}));
      }
    `}};function _lt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=ze.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=ze.computeOutShape(b,w,I),D=_i({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),P=bz(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new yC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var iG={kernelName:ml,backendName:"webgl",kernelFunc:_lt};function Elt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=wz(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var aG={kernelName:du,backendName:"webgl",kernelFunc:Elt};function Alt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=Iz(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var lG={kernelName:hu,backendName:"webgl",kernelFunc:Alt};function Dlt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=Cz(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var uG={kernelName:gu,backendName:"webgl",kernelFunc:Dlt};var $lt="return tan(x);",Rlt=It({opSnippet:$lt}),cG={kernelName:ii,backendName:"webgl",kernelFunc:Rlt};var Flt=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Olt=It({opSnippet:Flt}),pG={kernelName:ai,backendName:"webgl",kernelFunc:Olt};function Plt(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new rc(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var mG={kernelName:ll,backendName:"webgl",kernelFunc:Plt};var bC=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[i]*e[i];this.outputShape=n,this.rank=n.length;let o=zt(this.rank),s=Mlt(t);this.userCode=`
      void main() {
        ${o} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function Mlt(r){let t=r.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${e[o]}, ${r[o]})`);return n.join()}function G1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let u=e.readSync(o.dataId),l=o.dtype==="string"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=Sz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new bC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var fG={kernelName:lo,backendName:"webgl",kernelFunc:G1};var wC=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=t,this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];
 
         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.
 
         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;
 
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;
 
         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `}},IC=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=`
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];
 
         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.
 
         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
 
         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;
 
         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `}};function kp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function dG(r){let t=1;for(;t<r;)t*=2;return t}function Llt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n,a=L().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=L().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=o.shape,c=l[l.length-1];if(e.shouldExecuteOnCPU([o])||c<a||s>u){let P=e.readSync(o.dataId),[V,G]=Nz(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,"int32",[])];if(c===1)return[o,Hl({attrs:{shape:l,dtype:"int32",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&kp(e,f);let x=dG(s),b=dG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=I(),q=new wC(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],X=w;w=e.runWebGLProgram(q,W,"int32",K),kp(e,X)};for(let P=1;P<x;P*=2){let V=P*2;for(let G=P;G>=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=I(),G=new IC([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,"int32",q),kp(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let E=w;w=_i({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),kp(e,E);let A=O1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});kp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),kp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),kp(e,F),[A,w]}var hG={kernelName:fl,backendName:"webgl",kernelFunc:Llt};var CC=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${u} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${u} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${u} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }
 
            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${s});
              }
              return outputValue;
            }
 
            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${s});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${e}));
                float mapY = mapCoord(inY, float(${t}));
 
                if (${a} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `}};function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new CC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],"float32")}var gG={kernelName:dl,backendName:"webgl",kernelFunc:zlt};function Blt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ni(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=kz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var xG={kernelName:xu,backendName:"webgl",kernelFunc:Blt};function Vlt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;h<a;h++)h!==s&&(l[c++]=i.shape[h]);let p=[],m=new Array(a).fill(0),f=i.shape.slice();f[s]=1;let d=new Array(u);for(let h=0;h<d.length;h++){m[s]=h;let g=_i({inputs:{x:i},backend:e,attrs:{begin:m,size:f}}),x=rt({inputs:{x:g},backend:e,attrs:{shape:l}});d[h]=x,p.push(g)}return p.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var yG={kernelName:Xi,backendName:"webgl",kernelFunc:Vlt};var vC=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
        sumValue += dot(values, segFilter);
    `,f="";s%n>0&&(f=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);let d="";s%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${u};
 
      float getValue(int batch, int inIdx) {
        ${f}
        return getX(batch, inIdx);
      }
 
      float getSegmentIdAtIndex(int inIdx) {
        ${d}
        return getSegmentIds(inIdx);
      }
 
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${i})) * float(${n}));
        int currentSeg = int(mod(float(outIdx), float(${i})));
 
        float sumValue = 0.0;
 
        for (int i = 0; i < ${c}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );
 
          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );
 
          ${m}
        }
 
        int inIdx = inOffset + ${c};
        if (${p===1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );
 
          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
 
          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );
 
          ${m}
        } else if (${p===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );
 
          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );
 
          ${m}
        } else if (${p===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );
 
          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );
 
          ${m}
        }
        setOutput(${l});
      }
    `}};function Glt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=xc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],P=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(P,D),G={windowSize:V,inSize:P,batchSize:F,numSegments:D},W=new vC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=V1({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),K=G1({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(K),g(q,N,K,A,D)},x=g(d,"unsortedSegmentSum",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var bG={kernelName:yu,backendName:"webgl",kernelFunc:Glt};var Wlt=[e3,n3,o3,s3,a3,l3,u3,c3,f3,d3,h3,g3,x3,y3,b3,w3,I3,C3,v3,S3,N3,T3,_3,E3,A3,F3,P3,M3,Hz,z3,V3,G3,W3,U3,H3,q3,K3,j3,X3,Y3,Q3,tB,eB,rB,nB,oB,sB,iB,aB,lB,uB,cB,pB,mB,fB,dB,gB,xB,yB,bB,IB,CB,vB,SB,NB,kB,TB,_B,EB,Uz,AB,B3,DB,$B,RB,qz,FB,OB,PB,MB,LB,zB,BB,VB,GB,WB,HB,qB,KB,jB,XB,YB,JB,tV,eV,rV,nV,oV,uV,Xz,cV,pV,mV,fV,D3,dV,xV,yV,bV,wV,Kz,IV,CV,vV,SV,NV,$3,sV,kV,TV,_V,Zz,EV,AV,DV,$V,RV,FV,OV,PV,MV,LV,zV,BV,VV,GV,WV,UV,k3,lV,HV,qV,KV,jV,XV,YV,ZV,JV,tG,eG,nG,oG,sG,iG,aG,lG,uG,aV,Qz,cG,pG,mG,fG,hG,gG,t3,xG,yG,bG,hV];for(let r of Wlt)pc(r);var Nt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(Nt||(Nt={}));var nc;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(nc||(nc={}));var wG;function Ult(r){wG=r.wasm.cwrap(Zi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=nc[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return wG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var IG={kernelName:Zi,backendName:"wasm",setupFunc:Ult,kernelFunc:Hlt};function yt(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,Nt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var CG=yt($i);var vG=yt(qo);var SG=yt(Ko);function ee(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var qlt=!0,NG=ee(ao,qlt);var kG;function Klt(r){kG=r.wasm.cwrap(jo,null,["array","number","number","number"])}function jlt(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return kG(s,o.length,Nt[n.dtype],i),n}var TG={kernelName:jo,backendName:"wasm",setupFunc:Klt,kernelFunc:jlt};function Tp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype==="string")return sr(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var _G={kernelName:bo,backendName:"wasm",kernelFunc:Tp};var EG;function Xlt(r){EG=r.wasm.cwrap(uo,null,["number","array","number","number","number","array","number"])}function go(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Zlt(t.x.shape,n.perm),i=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(i=!1);let a=Ylt(t.x.shape,n.perm),u={dataId:t.x.dataId,shape:o,dtype:t.x.dtype};if(i){let d=Tp({inputs:t,backend:e});return d.shape=a,d}let l=e.makeOutput(a,u.dtype),c=e.dataIdMap.get(u.dataId).id,p=e.dataIdMap.get(l.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(u.shape).buffer);return EG(c,f,u.shape.length,Nt[u.dtype],p,m,s.length),l}function Ylt(r,t){let e=new Array(r.length);for(let n=0;n<e.length;n++)e[n]=r[t[n]];return e}function Zlt(r,t){let e=[],n=[];for(let o=0;o<r.length;++o)r[o]!==1&&e.push(r[o]),r[t[o]]!==1&&n.push(t[o]);for(let o=0;o<n.length;++o){let s=-1;for(let i=0;i<n.length;++i)n[i]>=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var AG={kernelName:uo,backendName:"wasm",kernelFunc:go,setupFunc:Xlt};function Sn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f<c.length;f++)c[f]=n[a[f]];i=S.getInnerMostAxes(i.length,o),u=go({inputs:{x:r},attrs:{perm:a},backend:e});let p=e.dataIdMap.get(r.dataId).id;e.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var DG;function Jlt(r){DG=r.wasm.cwrap(Ra,null,["number, number, number"])}function Qlt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("all",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;DG(u,x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var $G={kernelName:Ra,backendName:"wasm",setupFunc:Jlt,kernelFunc:Qlt};var RG;function tut(r){RG=r.wasm.cwrap(Fa,null,["number, number, number"])}function eut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("any",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;RG(u,x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var FG={kernelName:Fa,backendName:"wasm",setupFunc:tut,kernelFunc:eut};function SC(r){let t;function e(o){t=o.wasm.cwrap(r,null,["number","number","number","number","number"])}function n(o){let{backend:s,inputs:i,attrs:a}=o,{axis:u}=a,{x:l}=i,c=s.dataIdMap.get(l.dataId).id,p=c,m=l,{transposed:f,axes:d,inputWasTransposed:h}=Sn(l,u,s);if(h){let N=s.dataIdMap.get(f.dataId).id;N!==c&&(m=f,p=N)}let g=m.shape.slice(0,-1),x=s.makeOutput(g,"int32"),b=s.dataIdMap.get(x.dataId).id,w=y.sizeFromShape(x.shape),I=m.shape[d[0]];return t(p,Nt[m.dtype],w,I,b),h&&s.disposeData(f.dataId),x}return{kernelName:r,backendName:"wasm",setupFunc:e,kernelFunc:n}}var OG=SC(Ri);var PG=SC(Fi);var MG=yt(Xo);var LG=yt(Yo);var zG=yt(Zo);var BG=ee(Qo,!1);var VG=yt(Jo);var GG;function rut(r){GG=r.wasm.cwrap(ts,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nut(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.strideHeight,b=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let I=n.makeOutput(c.outShape,"float32"),N=n.dataIdMap.get(I.dataId).id;return GG(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,N),I}var WG={kernelName:ts,backendName:"wasm",setupFunc:rut,kernelFunc:nut};var UG;function out(r){UG=r.wasm.cwrap("AvgPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return UG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var HG={kernelName:Oi,backendName:"wasm",setupFunc:out,kernelFunc:sut};var qG;function iut(r){qG=r.wasm.cwrap("AvgPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function aut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return qG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left,c.filterDepth,c.filterHeight,c.filterWidth),p}var KG={kernelName:Jl,backendName:"wasm",setupFunc:iut,kernelFunc:aut};var jG;function lut(r){jG=r.wasm.cwrap("AvgPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function uut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u}=n,l=S.computePool2DInfo(s.shape,i,a,1,u),c=e.makeOutput(s.shape,s.dtype);return jG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.top,l.padInfo.left,l.filterHeight,l.filterWidth),c}var XG={kernelName:Zl,backendName:"wasm",setupFunc:lut,kernelFunc:uut};function mr(r){let{inputs:t,attrs:e}=r,{x:n}=t,{shape:o}=e,s=y.sizeFromShape(n.shape),i=y.inferFromImplicitShape(o,s);return y.assert(s===y.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var YG={kernelName:Ui,backendName:"wasm",kernelFunc:mr};var ZG;function cut(r){ZG=r.wasm.cwrap(es,null,["number","array","number","number","array","number","number","number","number"])}function put(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${i} and transposeB=${a} must match.`);let I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=mr({inputs:{x:o},backend:e,attrs:{shape:I}}),A=mr({inputs:{x:s},backend:e,attrs:{shape:N}}),D=e.dataIdMap.get(E.dataId).id,F=e.dataIdMap.get(A.dataId).id,P=i?E.shape[2]:E.shape[1],V=a?A.shape[1]:A.shape[2],G=Math.max(g,x),W=e.makeOutput([G,P,V],E.dtype),q=e.dataIdMap.get(W.dataId).id,H=new Uint8Array(new Int32Array(E.shape).buffer),K=new Uint8Array(new Int32Array(A.shape).buffer);return ZG(D,H,E.shape.length,F,K,A.shape.length,i,a,q),e.disposeData(E.dataId),e.disposeData(A.dataId),W.shape=w,W}var JG={kernelName:es,backendName:"wasm",setupFunc:cut,kernelFunc:put};function Go(r){let{inputs:{x:t},attrs:{begin:e,size:n},backend:o}=r,[s,i]=ze.parseSliceParams(t,e,n),a=ze.isSliceContinous(t.shape,s,i),u=o.readSync(t.dataId),l=o.makeOutput(i,t.dtype),c=y.computeStrides(t.shape),p=o.dataIdMap.get(l.dataId);if(a){let d=ze.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype==="string"){let d=cp(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)mut(u,c[0],m,s,i);else if(f===3)fut(u,c[0],c[1],m,s,i);else if(f===4)dut(u,c[0],c[1],c[2],m,s,i);else{let d=cp(u,s,i,t.shape,t.dtype);m.set(d)}return l}function mut(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;l<u;l++){let c=l*t+a;e.set(r.subarray(c,c+o[1]),s),s+=o[1]}}function fut(r,t,e,n,o,s){let i=0,a=o[0],u=o[1],l=o[2],c=a+s[0],p=u+s[1];for(let m=a;m<c;m++)for(let f=u;f<p;f++){let d=m*t+f*e+l;n.set(r.subarray(d,d+s[2]),i),i+=s[2]}}function dut(r,t,e,n,o,s,i){let a=0,u=s[0],l=s[1],c=s[2],p=u+i[0],m=l+i[1],f=c+i[2],d=s[3];for(let h=u;h<p;h++)for(let g=l;g<m;g++)for(let x=c;x<f;x++){let b=h*t+g*e+x*n+d;o.set(r.subarray(b,b+i[3]),a),a+=i[3]}}var QG={kernelName:qi,backendName:"wasm",kernelFunc:Go};function hut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n,a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=go({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Go({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var tW={kernelName:Pi,backendName:"wasm",kernelFunc:hut};var eW;function gut(r){eW=r.wasm.cwrap(Oa,null,["number","number","boolean","number","number","number"])}function xut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return eW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var rW={kernelName:Oa,backendName:"wasm",setupFunc:gut,kernelFunc:xut};var yut=!0,nW=ee(Pa,yut);function but(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],"int32",void 0,new Int32Array(a))}var oW={kernelName:Ql,backendName:"wasm",kernelFunc:but};function Mn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var sW={kernelName:xo,backendName:"wasm",kernelFunc:Mn};var iW=yt(rs);var aW;function wut(r){aW=r.wasm.cwrap(yo,null,["number","number","number","number"])}function Iut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return aW(a,s,i,l),u}var lW={kernelName:yo,backendName:"wasm",setupFunc:wut,kernelFunc:Iut};function W1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return Tp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return mr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=ap(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f<u;f++){let d=f*l;for(let h=0;h<p.length;h++){let g=c[h],x=f*g,b=p[h].subarray(x,x+g);m.set(b,d),d+=g}}return a}var uW={kernelName:Mi,backendName:"wasm",kernelFunc:W1};var cW;function Cut(r){cW=r.wasm.cwrap(ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:m}=e,f=S.convertConv2DDataFormat(m),d=S.computeConv2DInfo(o.shape,s.shape,u,l,c,p,!1,f),h=d.filterHeight,g=d.filterWidth,x=d.padInfo.top,b=d.padInfo.right,w=d.padInfo.bottom,I=d.padInfo.left,N=d.dilationHeight,E=d.dilationWidth,A=d.strideHeight,D=d.strideWidth,F=d.inChannels,P=d.outChannels,V=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let G=n.makeOutput(d.outShape,"float32"),W=n.dataIdMap.get(G.dataId).id;return cW(i,o.shape[0],o.shape[1],o.shape[2],a,h,g,x,b,w,I,V,N,E,A,D,F,P,W),G}var pW={kernelName:ns,backendName:"wasm",setupFunc:Cut,kernelFunc:vut};var mW;function Sut(r){mW=r.wasm.cwrap(os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nut(r){let{backend:t,inputs:e,attrs:n}=r,{dy:o,filter:s}=e,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,inputShape:c}=n,p=1,m=S.convertConv2DDataFormat(u),f=S.computeConv2DInfo(c,s.shape,i,p,a,l,!1,m),{batchSize:d,filterHeight:h,filterWidth:g,inChannels:x,inHeight:b,inWidth:w,outChannels:I,outHeight:N,outWidth:E,strideHeight:A,strideWidth:D}=f,F=h-1-f.padInfo.top,P=g-1-f.padInfo.left,V=f.dataFormat==="channelsLast",G=y.computeStrides(f.inShape),W=y.computeStrides(o.shape),[q,H,K]=y.computeStrides(s.shape),X=G[0],Z=V?G[1]:G[2],et=V?G[2]:1,nt=V?1:G[1],st=W[0],at=V?W[1]:W[2],ot=V?W[2]:1,it=V?1:W[1],mt=t.makeOutput(f.inShape,"float32"),gt=t.dataIdMap.get(mt.dataId).id,Ct=t.dataIdMap.get(o.dataId).id,Rt=t.dataIdMap.get(s.dataId).id;return mW(Ct,Rt,d,h,g,b,w,x,N,E,I,A,D,F,P,q,H,K,X,Z,et,nt,st,at,ot,it,gt),mt}var fW={kernelName:os,backendName:"wasm",setupFunc:Sut,kernelFunc:Nut};var dW;function kut(r){dW=r.wasm.cwrap(ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor x must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=e.makeOutput(l.outShape,o.dtype);return dW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var hW={kernelName:ss,backendName:"wasm",setupFunc:kut,kernelFunc:Tut};var gW;function _ut(r){gW=r.wasm.cwrap(Ma,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Eut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,u,i,1,a),c=e.makeOutput(l.filterShape,s.dtype);return gW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var xW={kernelName:Ma,backendName:"wasm",setupFunc:_ut,kernelFunc:Eut};var yW;function Aut(r){yW=r.wasm.cwrap(La,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(u,s.shape,a,1,i),c=e.makeOutput(l.inShape,o.dtype);return yW(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var bW={kernelName:La,backendName:"wasm",setupFunc:Aut,kernelFunc:Dut};var wW=yt(is);var IW=yt(as);var U1;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(U1||(U1={}));var CW;function $ut(r){CW=r.wasm.cwrap(Ba,null,["number","number","number","number","array","number","number","number","number","number"])}function Rut(r){let{backend:t,inputs:e,attrs:n}=r,{method:o,extrapolationValue:s,cropSize:i}=n,{image:a,boxes:u,boxInd:l}=e,c=u.shape[0],[p,m]=i,f=[c,p,m,a.shape[3]],d=t.dataIdMap.get(a.dataId),h;a.dtype!=="float32"&&(h=Mn({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),d=t.dataIdMap.get(h.dataId));let g=d.id,x=t.dataIdMap.get(u.dataId).id,b=t.dataIdMap.get(l.dataId).id,w=t.makeOutput(f,"float32"),I=t.dataIdMap.get(w.dataId).id,N=new Uint8Array(new Int32Array(a.shape).buffer);return CW(g,x,b,c,N,p,m,U1[o],s,I),h!=null&&t.disposeData(h.dataId),w}var vW={kernelName:Ba,backendName:"wasm",setupFunc:$ut,kernelFunc:Rut};var SW;function Fut(r){SW=r.wasm.cwrap(za,null,["number","number","number","number","number","number"])}function Out(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=go({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;SW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var NW={kernelName:za,backendName:"wasm",setupFunc:Fut,kernelFunc:Out};var kW;function Put(r){kW=r.wasm.cwrap(ls,null,["number","number","number","number","number","number"])}function Mut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=go({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;kW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var TW={kernelName:ls,backendName:"wasm",setupFunc:Put,kernelFunc:Mut};var _W;function Lut(r){_W=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function zut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return _W(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var EW={kernelName:eu,backendName:"wasm",setupFunc:Lut,kernelFunc:zut};var AW;function But(r){AW=r.wasm.cwrap(Va,null,["number","number","number","array","number","array","array","number","number"])}function Vut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return AW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var DW={kernelName:Va,backendName:"wasm",setupFunc:But,kernelFunc:Vut};var $W;function Gut(r){$W=r.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,"float32"),G=n.dataIdMap.get(V.dataId).id;return $W(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,P,I,N,E,A,D,F,G),V}var RW={kernelName:us,backendName:"wasm",setupFunc:Gut,kernelFunc:Wut};var FW;function Uut(r){FW=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function Hut(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return FW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var OW={kernelName:ru,backendName:"wasm",setupFunc:Uut,kernelFunc:Hut};var PW;function qut(r){PW=r.wasm.cwrap(cs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c=e.makeOutput(l.outShape,o.dtype);return PW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,Nt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var MW={kernelName:cs,backendName:"wasm",setupFunc:qut,kernelFunc:Kut};var LW;function jut(r){LW=r.wasm.cwrap(ou,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Xut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return LW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var zW={kernelName:ou,backendName:"wasm",setupFunc:jut,kernelFunc:Xut};var BW;function Yut(r){BW=r.wasm.cwrap(nu,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return BW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var VW={kernelName:nu,backendName:"wasm",setupFunc:Yut,kernelFunc:Zut};var GW=yt(ms);var WW;function Jut(r){WW=r.wasm.cwrap(Ga,null,["number","number","number"])}function Qut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return WW(i(o),i(n),i(s)),s}var UW={kernelName:Ga,backendName:"wasm",setupFunc:Jut,kernelFunc:Qut};var tct=!1,HW=ee(Wa,tct,"bool");var qW=yt(fs);var KW=yt(ds,"float32");function NC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var jW={kernelName:Li,backendName:"wasm",kernelFunc:NC};var XW=yt(hs,"float32");function H1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var YW={kernelName:su,backendName:"wasm",kernelFunc:H1};var ZW;function ect(r){ZW=r.wasm.cwrap(Ua,null,["number","number","number","number","number","number"])}function rct(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return ZW(s,a,u,l,c,i),o}var JW={kernelName:Ua,backendName:"wasm",kernelFunc:rct,setupFunc:ect};var QW=yt(gs);var nct=!1,tU=ee(xs,nct);var eU;function oct(r){eU=r.wasm.cwrap(ys,null,["number","number","number","number","number","number","number"])}function sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return eU(c,p,m,f,d,o,g),h}var rU={kernelName:ys,backendName:"wasm",setupFunc:oct,kernelFunc:sct};var nU;function ict(r){nU=r.wasm.cwrap(Ji,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function act(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=nc[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return nU(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var oU={kernelName:Ji,backendName:"wasm",setupFunc:ict,kernelFunc:act};var sU;function lct(r){sU=r.wasm.cwrap(Qi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function uct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=nc[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return sU(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var iU={kernelName:Qi,backendName:"wasm",setupFunc:lct,kernelFunc:uct};var aU;function cct(r){aU=r.wasm.cwrap(Ha,null,["number","number","number","number","number","number","array","number"])}function pct(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Dy.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return aU(f,Nt[n.dtype],h,i,p,a,g,x),l}var lU={kernelName:Ha,backendName:"wasm",setupFunc:cct,kernelFunc:pct};var uU;function mct(r){uU=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function fct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F<l.length;++F){let P=l[F];y.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return uU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var cU={kernelName:zi,backendName:"wasm",setupFunc:mct,kernelFunc:fct};var dct=!1,pU=ee(qa,dct,"bool");var hct=!1,mU=ee(bs,hct,"bool");var fU=yt(ws,"bool");var dU=yt(Is,"bool");var hU=yt(Cs,"bool");var gU;function gct(r){gU=r.wasm.cwrap(vs,null,["number","number","number","number"])}function xct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;gU(o,Nt[t.dtype],e,i)}return s}var xU={kernelName:vs,backendName:"wasm",setupFunc:gct,kernelFunc:xct};var yct=!1,yU=ee(Ka,yct,"bool");var bct=!1,bU=ee(ja,bct,"bool");var wU;function wct(r){wU=r.wasm.cwrap(Xa,null,["number","number","number","number"])}function Ict(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return wU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var IU={kernelName:Xa,backendName:"wasm",setupFunc:wct,kernelFunc:Ict};var CU=yt(Ss);var vU=yt(Ns);var Cct=!1,SU=ee(Ya,Cct,"bool");var NU=yt(Za);var vct=!1,kU=ee(Ja,vct,"bool");var Sct=!1,TU=ee(v_,Sct,"bool");var _U;function Nct(r){_U=r.wasm.cwrap(ks,null,["number","number","number","number","number","number","number"])}function kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let l=e.makeOutput(o.shape,o.dtype);return _U(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var EU={kernelName:ks,backendName:"wasm",setupFunc:Nct,kernelFunc:kct};var AU;function Tct(r){AU=r.wasm.cwrap(Qa,null,["number","number","number","number","number","number","number","number","number"])}function _ct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return AU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var DU={kernelName:Qa,backendName:"wasm",setupFunc:Tct,kernelFunc:_ct};var $U;function Ect(r){$U=r.wasm.cwrap(Ts,null,["number","number","number","number"])}function Act(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;$U(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var RU={kernelName:Ts,backendName:"wasm",setupFunc:Ect,kernelFunc:Act};var Dct=!1,FU=ee(_s,Dct);var OU;function $ct(r){OU=r.wasm.cwrap(Es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Rct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,"float32"),D=n.dataIdMap.get(A.dataId).id;return OU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var PU={kernelName:Es,backendName:"wasm",setupFunc:$ct,kernelFunc:Rct};var MU;function Fct(r){MU=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Oct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return MU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var LU={kernelName:Bi,backendName:"wasm",setupFunc:Fct,kernelFunc:Oct};var zU;function Pct(r){zU=r.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mct(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return zU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var BU={kernelName:au,backendName:"wasm",setupFunc:Pct,kernelFunc:Mct};var VU;function Lct(r){VU=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zct(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return VU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var GU={kernelName:iu,backendName:"wasm",setupFunc:Lct,kernelFunc:zct};var WU;function Bct(r){WU=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,"int32");return WU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var UU={kernelName:lu,backendName:"wasm",setupFunc:Bct,kernelFunc:Vct};var HU;function Gct(r){HU=r.wasm.cwrap(As,null,["number, number, number"])}function Wct(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("mean",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!=="float32"&&(b=Mn({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,"float32");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;HU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!=="float32"&&t.disposeData(b.dataId),w}var qU={kernelName:As,backendName:"wasm",setupFunc:Gct,kernelFunc:Wct};var KU;function Uct(r){KU=r.wasm.cwrap(Ds,null,["number","number","number","number"])}function Hct(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let d=l.shape.length;S.assertAxesAreInnerMostDims("min",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;KU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var jU={kernelName:Ds,backendName:"wasm",setupFunc:Uct,kernelFunc:Hct};var qct=!1,XU=ee($s,qct);var q1;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(q1||(q1={}));var YU;function Kct(r){YU=r.wasm.cwrap(Rs,null,["number","array","number","number","array","array","number","number"])}function jct(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return YU(i,l,t.shape.length,Nt[t.dtype],m,f,q1[o],u),a}var ZU={kernelName:Rs,backendName:"wasm",kernelFunc:jct,setupFunc:Kct};var JU;function Xct(r){JU=r.wasm.cwrap(ni,null,["number","number","number","number"])}function K1(r){let{backend:t,inputs:{logits:e},attrs:{dim:n}}=r,o=t.dataIdMap.get(e.dataId).id,s=t.makeOutput(e.shape,e.dtype),i=t.dataIdMap.get(s.dataId).id,a=e.shape[n],u=y.sizeFromShape(e.shape)/a;return y.sizeFromShape(s.shape)===0||JU(o,i,a,u),s}var QU={kernelName:ni,backendName:"wasm",setupFunc:Xct,kernelFunc:K1};var t4;function Yct(r){t4=r.wasm.cwrap(tl,null,["number","number","number","number","number","number"])}function Zct(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n;if(o.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${o.dtype}`);let u=a?o:K1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),[l,c]=u.shape,p=e.makeOutput([l,s],"int32");return t4(e.dataIdMap.get(u.dataId).id,l,c,s,i,e.dataIdMap.get(p.dataId).id),a||e.disposeData(u.dataId),p}var e4={kernelName:tl,backendName:"wasm",setupFunc:Yct,kernelFunc:Zct};var r4=ee(Fs,!0);var Jct=!0,n4=ee(Os,Jct);var o4=yt(Vi);function Hd(r,t){let e=new Int32Array(r.wasm.HEAPU8.buffer,t,4),n=e[0],o=e[1],s=e[2],i=e[3];return r.wasm._free(t),{pSelectedIndices:n,selectedSize:o,pSelectedScores:s,pValidOutputs:i}}var s4;function Qct(r){s4=r.wasm.cwrap(rl,"number",["number","number","number","number","number"])}function tpt(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i}=n,{boxes:a,scores:u}=e,l=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=s4(l,c,s,o,i),{pSelectedIndices:m,selectedSize:f,pSelectedScores:d,pValidOutputs:h}=Hd(t,p);return t.wasm._free(d),t.wasm._free(h),t.makeOutput([f],"int32",m)}var i4={kernelName:rl,backendName:"wasm",setupFunc:Qct,kernelFunc:tpt};var a4;function ept(r){a4=r.wasm.cwrap(nl,"number",["number","number","number","number","number","bool"])}function rpt(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=a4(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Hd(t,m);t.wasm._free(h);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([],"int32",g);return[x,b]}var l4={kernelName:nl,backendName:"wasm",setupFunc:ept,kernelFunc:rpt};var u4;function npt(r){u4=r.wasm.cwrap(ol,"number",["number","number","number","number","number","number"])}function opt(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,softNmsSigma:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=u4(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Hd(t,m);t.wasm._free(g);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([d],"float32",h);return[x,b]}var c4={kernelName:ol,backendName:"wasm",setupFunc:npt,kernelFunc:opt};var spt=!1,p4=ee(el,spt,"bool");var m4;function ipt(r){m4=r.wasm.cwrap(Ps,null,["number","number","number","number","number"])}function apt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=e.makeOutput([...o.shape,i],s),c=e.dataIdMap.get(l.dataId).id,m=e.dataIdMap.get(o.dataId).id;return m4(m,i,a,u,c),l}var f4={kernelName:Ps,backendName:"wasm",setupFunc:ipt,kernelFunc:apt};function lpt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(1),n}var d4={kernelName:Gi,backendName:"wasm",kernelFunc:lpt};function upt(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return NC({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=NC({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=W1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeData(c.dataId)),l}var h4={kernelName:Wi,backendName:"wasm",kernelFunc:upt};var g4;function cpt(r){g4=r.wasm.cwrap(Ms,null,["number","array","number","number","array","array","number","number"])}function ppt(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,constantValue:o}}=r,s=n.map((h,g)=>h[0]+t.shape[g]+h[1]);if(y.sizeFromShape(t.shape)===0)return H1({backend:e,attrs:{shape:s,value:o,dtype:t.dtype}});let i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),l=e.dataIdMap.get(a.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(h=>h[0]),m=n.map(h=>h[1]),f=new Uint8Array(new Int32Array(p).buffer),d=new Uint8Array(new Int32Array(m).buffer);return g4(i,c,t.shape.length,Nt[t.dtype],f,d,o,l),a}var kC={kernelName:Ms,backendName:"wasm",kernelFunc:ppt,setupFunc:cpt};var mpt=!1,x4=ee(Ls,mpt);var y4;function fpt(r){y4=r.wasm.cwrap(zs,null,["number","number","number"])}function dpt(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,a=s,u=n,l=u;u.dtype!=="float32"&&(l=Mn({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),a=e.dataIdMap.get(l.dataId).id);let c=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(c.dataId).id;return y4(a,i,p),u.dtype!=="float32"&&e.disposeData(l.dataId),c}var b4={kernelName:zs,backendName:"wasm",setupFunc:fpt,kernelFunc:dpt};var w4;function hpt(r){w4=r.wasm.cwrap(Bs,null,["number","number","number","number"])}function gpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t),d=p;if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&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liceSize:c,strides:p,outputSize:m}=Mu.calculateShapes(s,o,i),d=t.dataIdMap.get(o.dataId).id,g=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(p).buffer),b=t.dataIdMap.get(a.dataId).id;return V4(d,g,Nt[s.dtype],u,l,c,x,m,b),a}var G4={kernelName:al,backendName:"wasm",setupFunc:Dpt,kernelFunc:$pt};var W4;function Rpt(r){W4=r.wasm.cwrap(ul,null,["number","number","number","number","number","number","bool","number"])}function Fpt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n;if(o.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${o.dtype} and ${s.dtype}`);let a=e.makeOutput(s.shape,"int32");function u(l){return e.dataIdMap.get(l.dataId).id}return W4(u(o),u(s),o.shape[0],o.shape[1],s.shape[1],Nt[o.dtype],i==="left",u(a)),a}var U4={kernelName:ul,backendName:"wasm",setupFunc:Rpt,kernelFunc:Fpt};var H4;function Opt(r){H4=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Ppt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=e.dataIdMap.get(n.dataId).id,a=e.dataIdMap.get(o.dataId).id,u=e.dataIdMap.get(s.dataId).id,l=e.makeOutput(o.shape,o.dtype),c=e.dataIdMap.get(l.dataId).id,p=n.shape.length,m=o.shape.length,f=p===0||p>1||m===1?1:y.sizeFromShape(o.shape.slice(1));return H4(i,a,u,f,c),l}var q4={kernelName:Hi,backendName:"wasm",kernelFunc:Ppt,setupFunc:Opt};var K4=yt(Xs);var j4;function Mpt(r){j4=r.wasm.cwrap(Qs,null,["number","number"])}function Lpt(r){let{backend:t,inputs:{x:e}}=r,n=t.dataIdMap.get(e.dataId).id,o=t.makeOutput(e.shape,e.dtype),s=t.dataIdMap.get(o.dataId).id;return y.sizeFromShape(o.shape)===0||j4(n,s),o}var X4={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Mpt,kernelFunc:Lpt};var Y4=yt(Js);var Z4=yt(Ys);var J4=yt(Zs);var Q4=yt(ti);function zpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n,a=y.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let E=1+s.length;E<o.shape.length;++E)u.push([0,0]);let l=kC.kernelFunc({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,s,a,!1),p=S.getPermuted(c.length,s.length,!1),m=S.getReshapedPermuted(l.shape,s,a,!1),h=mr({inputs:{x:l},backend:e,attrs:{shape:c}}),b=go({inputs:{x:h},backend:e,attrs:{perm:p}}),N=mr({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeData(l.dataId),e.disposeData(h.dataId),e.disposeData(b.dataId),N}var tH={kernelName:Ki,backendName:"wasm",kernelFunc:zpt};var eH;function Bpt(r){eH=r.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Vpt(r){let{backend:t,inputs:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=e,a=n.shape[0],u=n.shape[1],l=t.readSync(s.dataId)[0],c=[a+l,u],p=t.dataIdMap.get(n.dataId).id,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(i.dataId).id,d=t.makeOutput(c,n.dtype),h=t.dataIdMap.get(d.dataId).id,g=t.makeOutput(c.slice(0,1),o.dtype),x=t.dataIdMap.get(g.dataId).id,b=t.makeOutput([l],"bool"),w=t.dataIdMap.get(b.dataId).id,I=t.makeOutput([a],n.dtype),N=t.dataIdMap.get(I.dataId).id,E=t.makeOutput([4],"int32"),A=t.dataIdMap.get(E.dataId).id,D=eH(p,m,Nt[o.dtype],a,l,u,f,h,x,w,N,A),F=t.readSync(E.dataId),P;switch(F[0]){case 1:{P=S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(F[1]);break}case 2:{P=S.getSparseFillEmptyRowsNegativeIndexErrorMessage(F[1],F[2]);break}case 3:P=S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(F[1],F[2],F[3]);break;default:P=""}if(t.disposeData(E.dataId),P)throw t.disposeData(d.dataId),t.disposeData(g.dataId),t.disposeData(b.dataId),t.disposeData(I.dataId),new Error(P);let V=d,G=g;return D!==c[0]&&(V=Go({inputs:{x:d},attrs:{begin:0,size:[D,u]},backend:t}),G=Go({inputs:{x:g},attrs:{begin:0,size:D},backend:t}),t.disposeData(d.dataId),t.disposeData(g.dataId)),[V,G,b,I]}var rH={kernelName:cu,backendName:"wasm",setupFunc:Bpt,kernelFunc:Vpt};var nH;function Gpt(r){nH=r.wasm.cwrap(cl,null,["number","number","number","number","number","number","number"])}function Wpt(r){let{backend:t,inputs:e}=r,{inputIndices:n,inputShape:o,newShape:s}=e;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=t.dataIdMap.get(n.dataId).id,a=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(s.dataId).id,l=n.shape[0],c=y.sizeFromShape(s.shape),p=t.makeOutput([l,c],n.dtype),m=t.dataIdMap.get(p.dataId).id,f=t.makeOutput([c],s.dtype),d=t.dataIdMap.get(f.dataId).id,h=t.makeOutput([3],"int32"),g=t.dataIdMap.get(h.dataId).id;nH(i,a,u,l,m,d,g);let x=t.readSync(h.dataId),b;switch(x[0]){case 0:{b=S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1],x[2]);break}case 1:{b=S.getSparseReshapeNegativeOutputDimErrorMessage(x[1],x[2]);break}case 2:b=S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let w=Array.from(t.readSync(o.dataId)),I=Array.from(t.readSync(f.dataId));b=S.getSparseReshapeInputOutputMultipleErrorMessage(w,I);break}case 4:{let w=Array.from(t.readSync(o.dataId)),I=Array.from(t.readSync(f.dataId));b=S.getSparseReshapeInputOutputMismatchErrorMessage(w,I);break}default:b=""}if(t.disposeData(h.dataId),b)throw t.disposeData(p.dataId),t.disposeData(f.dataId),new Error(b);return[p,f]}var oH={kernelName:cl,backendName:"wasm",setupFunc:Gpt,kernelFunc:Wpt};var sH;function TC(r){sH=r.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function _C(r,t){let{backend:e,inputs:n}=r,{data:o,indices:s,segmentIds:i}=n,a=s.shape[0],u=e.readSync(i.dataId,a-1,a)[0],c=a>0?u+1:0;if(c<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=o.shape.slice();p[0]=c;let m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=e.dataIdMap.get(i.dataId).id,h=e.makeOutput(p,o.dtype),g=e.dataIdMap.get(h.dataId).id,x=e.makeOutput([4],"int32"),b=e.dataIdMap.get(x.dataId).id;sH(m,Nt[o.dtype],o.shape[0],f,d,g,b,t,0);let w=e.readSync(x.dataId),I;switch(w[0]){case 0:{I=S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{I=S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:I=S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(w[1],w[2]);break;case 3:I=S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w[1],w[2],w[3]);break;default:I=""}if(e.disposeData(x.dataId),I)throw e.disposeData(h.dataId),new Error(I);return h}function Upt(r){return _C(r,!0)}var iH={kernelName:pu,backendName:"wasm",setupFunc:TC,kernelFunc:Upt};function Hpt(r){return _C(r,!1)}var aH={kernelName:mu,backendName:"wasm",setupFunc:TC,kernelFunc:Hpt};var lH;function qpt(r){lH=r.wasm.cwrap(pl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Kpt(r){let{backend:t,inputs:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=e,{outputShape:a}=n,u=t.makeOutput(a,i.dtype);if(y.sizeFromShape(a)===0)return u;let{sliceRank:l,numUpdates:c,sliceSize:p,strides:m,outputSize:f}=S.calculateShapes(s,o,a),d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,x=new Uint8Array(new Int32Array(m).buffer),b=t.dataIdMap.get(u.dataId).id;return lH(d,h,s.shape.length,g,Nt[i.dtype],l,c,p,x,f,b),u}var uH={kernelName:pl,backendName:"wasm",setupFunc:qpt,kernelFunc:Kpt};function jpt(r){let{inputs:t,attrs:e,backend:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=e,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=new Array(o.shape.length).fill(0),c=o.shape.slice();return u.map(p=>{let m=[...c];m[a]=p;let f=Go({inputs:{x:o},attrs:{begin:l,size:m},backend:n});return l[a]+=p,f})}var cH={kernelName:ji,backendName:"wasm",kernelFunc:jpt};var pH=yt(ei);var mH=yt(fu);var Xpt=!0,fH=ee(oi,Xpt);var dH;function Ypt(r){dH=r.wasm.cwrap(wo,null,["number","number","number","number"])}function Zpt(r){let{backend:t,inputs:e,attrs:n}=r,{alpha:o}=n,{x:s}=e,i=t.dataIdMap.get(s.dataId).id,a=t.makeOutput(s.shape,s.dtype),u=t.dataIdMap.get(a.dataId).id;return dH(i,o,Nt[s.dtype],u),a}var hH={kernelName:wo,backendName:"wasm",setupFunc:Ypt,kernelFunc:Zpt};var gH;function Jpt(r){gH=r.wasm.cwrap(ml,null,["number","array","number","array","array","array","array","array","number","number"])}function Qpt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=ze.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=mr({inputs:{x:o},backend:t,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let E=ze.computeOutShape(b,w,I),A=Go({inputs:{x:o},backend:t,attrs:{begin:b,size:E}});N=mr({inputs:{x:A},backend:t,attrs:{shape:d}}),t.disposeData(A.dataId)}else{let E=t.makeOutput(f,"float32"),A=t.dataIdMap.get(o.dataId).id,D=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),F=new Uint8Array(new Int32Array(b).buffer),P=new Uint8Array(new Int32Array(w).buffer),V=new Uint8Array(new Int32Array(I).buffer),G=new Uint8Array(new Int32Array(f).buffer),W=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),q=t.dataIdMap.get(E.dataId).id;gH(A,D,o.shape.length,F,P,V,G,W,f.length,q),N=mr({inputs:{x:E},backend:t,attrs:{shape:d}}),t.disposeData(E.dataId)}return N}var xH={kernelName:ml,backendName:"wasm",setupFunc:Jpt,kernelFunc:Qpt};function tmt(r){let{backend:t,inputs:e,attrs:n}=r,{data:o,dataSplits:s}=e,{separator:i,nGramWidths:a,leftPad:u,rightPad:l,padWidth:c,preserveShortSequences:p}=n,m=t.readSync(o.dataId),f=t.readSync(s.dataId),[d,h]=pp(m,f,i,a,u,l,c,p),g=t.makeOutput([d.length],"string"),x=t.dataIdMap.get(g.dataId);x.stringBytes=d;let b=t.makeOutput(s.shape,"int32");return t.typedArrayFromHeap(b).set(h),[g,b]}var yH={kernelName:du,backendName:"wasm",kernelFunc:tmt};function emt(r){let{backend:t,inputs:e,attrs:n}=r,{input:o,delimiter:s}=e,{skipEmpty:i}=n,a=t.readSync(o.dataId),u=t.readSync(s.dataId),[l,c,p]=mp(a,u[0],i),m=c.length,f=t.makeOutput([m,2],"int32");t.typedArrayFromHeap(f).set(l);let h=t.makeOutput([m],"string"),g=t.dataIdMap.get(h.dataId);g.stringBytes=c;let x=t.makeOutput([2],"int32");return t.typedArrayFromHeap(x).set(p),[f,h,x]}var bH={kernelName:hu,backendName:"wasm",kernelFunc:emt};function rmt(r){let{backend:t,inputs:e,attrs:n}=r,{input:o}=e,{numBuckets:s}=n,i=t.readSync(o.dataId),a=fp(i,s),u=t.makeOutput(o.shape,"int32");return t.typedArrayFromHeap(u).set(a),u}var wH={kernelName:gu,backendName:"wasm",kernelFunc:rmt};var nmt=!0,IH=ee(si,nmt);var CH;function omt(r){CH=r.wasm.cwrap(ri,null,["number","number","number","number"])}function smt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t),d=p;if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("sum",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;CH(u,x,Nt[b.dtype],w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var vH={kernelName:ri,backendName:"wasm",setupFunc:omt,kernelFunc:smt};var SH=yt(ii);var NH=yt(ai);var kH;function imt(r){kH=r.wasm.cwrap(ll,null,["number","number","number","number","number","number","array","number","number","number"])}function amt(r){let{backend:t,inputs:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=e,{}=n,a=t.makeOutput(o.shape,o.dtype);if(y.sizeFromShape(o.shape)===0)return a;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=Mu.calculateShapes(i,s,o.shape),d=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,b=t.dataIdMap.get(o.dataId).id,w=new Uint8Array(new Int32Array(p).buffer),I=t.dataIdMap.get(a.dataId).id;return kH(d,g,Nt[i.dtype],u,l,c,w,m,I,b),a}var TH={kernelName:ll,backendName:"wasm",setupFunc:imt,kernelFunc:amt};var _H;function lmt(r){_H=r.wasm.cwrap(lo,null,["number","array","number","array","number","number"])}function umt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,s=e.dataIdMap.get(o.dataId).id,{reps:i}=n,a=new Array(o.shape.length);for(let m=0;m<a.length;m++)a[m]=o.shape[m]*i[m];let u=new Uint8Array(new Int32Array(o.shape).buffer),l=new Uint8Array(new Int32Array(a).buffer),c=e.makeOutput(a,o.dtype),p=e.dataIdMap.get(c.dataId).id;return _H(s,u,o.shape.length,l,a.length,Nt[c.dtype],p),c}var EH={kernelName:lo,backendName:"wasm",setupFunc:lmt,kernelFunc:umt};var AH;function cmt(r){AH=r.wasm.cwrap(fl,null,["number","array","number","number","number","bool","number","number"])}var pmt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{k:o,sorted:s}=e,i=t.dataIdMap.get(n.dataId).id,a=new Uint8Array(new Int32Array(n.shape).buffer),u=n.shape.slice();u[u.length-1]=o;let l=t.makeOutput(u,n.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.makeOutput(u,"int32"),m=t.dataIdMap.get(p.dataId).id;return AH(i,a,n.shape.length,Nt[n.dtype],o,s,c,m),[l,p]},DH={kernelName:fl,backendName:"wasm",setupFunc:cmt,kernelFunc:pmt};var $H;function mmt(r){$H=r.wasm.cwrap(dl,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function fmt(r){let{backend:t,inputs:e,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),b=new Uint8Array(new Int32Array(y.computeStrides(g)).buffer),w=t.makeOutput(g,o.dtype),I=t.dataIdMap.get(w.dataId).id,E=t.dataIdMap.get(o.dataId).id,D=t.dataIdMap.get(s.dataId).id,F=i==="nearest"?1:2,P;switch(a){case"constant":P=1;break;case"reflect":P=2;break;case"wrap":P=3;break;case"nearest":P=4;break;default:P=1;break}return $H(E,D,s.shape[0]>1,c,d,h,f,m,p,x,o.shape.length-1,b,g.length-1,F,P,u,I),w}var RH={kernelName:dl,backendName:"wasm",setupFunc:mmt,kernelFunc:fmt};function dmt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t,{outputValues:i,outputShape:a,indices:u}=dp(n.readSync(s.dataId),o,s.shape,s.dtype);return[n.makeOutput(a,s.dtype,void 0,i),n.makeOutput([u.length],"int32",void 0,u)]}var FH={kernelName:xu,backendName:"wasm",kernelFunc:dmt};function hmt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o.shape[s],a=o.shape.length,u=new Array(a-1),l=0;for(let f=0;f<a;f++)f!==s&&(u[l++]=o.shape[f]);let c=new Array(i),p=new Array(a).fill(0),m=o.shape.slice();m[s]=1;for(let f=0;f<c.length;f++)p[s]=f,c[f]=Go({inputs:{x:o},attrs:{begin:p,size:m},backend:e});return c.map(({dataId:f,dtype:d})=>({dataId:f,dtype:d,shape:u}))}var OH={kernelName:Xi,backendName:"wasm",kernelFunc:hmt};function gmt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(0),n}var PH={kernelName:Yi,backendName:"wasm",kernelFunc:gmt};var xmt=[IG,CG,vG,SG,NG,TG,$G,FG,OG,PG,MG,LG,zG,BG,VG,WG,XG,HG,KG,JG,tW,rW,nW,oW,sW,iW,lW,uW,pW,fW,hW,xW,bW,wW,IW,vW,NW,TW,EW,DW,RW,OW,MW,zW,VW,GW,UW,HW,qW,KW,jW,XW,YW,JW,QW,tU,rU,oU,iU,lU,cU,pU,mU,_G,fU,dU,hU,xU,yU,bU,IU,vU,CU,SU,NU,kU,TU,EU,DU,RU,FU,PU,LU,BU,GU,UU,qU,jU,XU,ZU,e4,r4,n4,o4,i4,l4,c4,p4,f4,d4,h4,kC,x4,b4,I4,C4,v4,S4,N4,k4,YG,_4,A4,$4,F4,P4,L4,z4,B4,G4,U4,q4,K4,X4,Y4,Z4,J4,QG,QU,Q4,tH,rH,oH,iH,aH,uH,cH,pH,mH,fH,hH,xH,yH,bH,wH,IH,vH,SH,NH,TH,EH,DH,RH,AG,FH,OH,PH];for(let r of xmt)pc(r);var j1=L();j1.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11]))}catch(r){return!1}});j1.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(j1.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(r){return!1}});var e_=Xl(BH()),qH=Xl(GH()),r_=Xl(WH());var UH=e_.default||e_,ymt=r_.default||r_,Ig=class extends Uo{constructor(t){super(),this.wasm=t,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(jH),t_=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Da(this,Wn())}write(t,e,n){let o={id:this.dataIdNextNumber++};return this.move(o,t,e,n,1),o}numDataIds(){return this.dataIdMap.numDataIds()}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}move(t,e,n,o,s){let i=this.dataIdNextNumber++;if(o==="string"){let c=e;this.dataIdMap.set(t,{id:i,stringBytes:c,shape:n,dtype:o,memoryOffset:null,refCount:s});return}let a=y.sizeFromShape(n),u=a*y.bytesPerElement(o),l=this.wasm._malloc(u)>>>0;this.dataIdMap.set(t,{id:i,memoryOffset:l,shape:n,dtype:o,refCount:s}),this.wasm.tfjs.registerTensor(i,a,l),e!=null&&this.wasm.HEAPU8.set(new Uint8Array(e.buffer,e.byteOffset,u),l)}async read(t){return this.readSync(t)}readSync(t,e,n){let{memoryOffset:o,dtype:s,shape:i,stringBytes:a}=this.dataIdMap.get(t);if(s==="string")return(e==null||e===0)&&(n==null||n>=a.length)?a:a.slice(e,n);e=e||0,n=n||y.sizeFromShape(i);let u=y.bytesPerElement(s),l=this.wasm.HEAPU8.slice(o+e*u,o+n*u);return wmt(l.buffer,s)}disposeData(t,e=!1){if(this.dataIdMap.has(t)){let n=this.dataIdMap.get(t);if(n.refCount--,!e&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(t)}return!0}refCount(t){return this.dataIdMap.has(t)?this.dataIdMap.get(t).refCount:0}incRef(t){let e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,n,o){let s;if(n==null)s=this.write(o!=null?o:null,t,e);else{let i=this.dataIdNextNumber++;s={id:i},this.dataIdMap.set(s,{id:i,memoryOffset:n,shape:t,dtype:e,refCount:1});let a=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(i,a,n)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:n}){let o=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),i=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(o,s,i);case"int32":return new Int32Array(o,s,i);case"bool":return new Uint8Array(o,s,i);default:throw new Error(`Unknown dtype ${e}`)}}};function bmt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function HH(r,t,e){if(DC!=null)return DC;let n="tfjs-backend-wasm.wasm";return r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),bg!=null&&bg[n]!=null?bg[n]:e+n}async function KH(){let[r,t]=await Promise.all([L().getAsync("WASM_HAS_SIMD_SUPPORT"),L().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile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split,Ne as sqrt,Wt as square,_m as squaredDifference,qn as squeeze,qe as stack,To as step,cy as stridedSlice,Y8 as string,lt as sub,pt as sum,xc as sumOutType,py as tan,ia as tanh,sr as tensor,Ke as tensor1d,fi as tensor2d,my as tensor3d,vA as tensor4d,SA as tensor5d,NA as tensor6d,TA as tensorScatterUpdate,So as tensor_util,dA as test_util,B as tidy,Or as tile,mht as time,fy as topk,zc as train,Vt as transpose,Am as truncatedNormal,dy as unique,Kmt as unregisterGradient,qmt as unregisterKernel,Dm as unsortedSegmentSum,xr as unstack,ur as upcastType,_A as upperBound,y as util,z6 as valueAndGrad,B6 as valueAndGrads,hy as variable,Kx as variableGrads,$mt as version,DF as version_converter,B2 as version_core,MO as version_cpu,ef as version_layers,kmt as version_wasm,Gz as version_webgl,JDe as webgl,_d as webgl_util,be as where,xy as whereAsync,Te as zeros,vt as zerosLike};