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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=We().readSync(this.dataId);if("string"===this.dtype)try{return e.map(e=>Re(e))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await We().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(We().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Ue.print(this,e)}clone(){return this.throwIfDisposed(),Ue.clone(this)}toString(e=!1){return Le(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ue.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),We().makeVariable(this,e,t,n)}}Object.defineProperty(Ge,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),O("Tensor",()=>Ge);class Ve extends Ge{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!c(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);We().disposeTensor(this),this.dataId=e.dataId,We().incRef(this,null)}dispose(){We().disposeVariable(this),this.isDisposedInternal=!0}}var He,je,Ze,Je,Ye;Object.defineProperty(Ve,Symbol.hasInstance,{value:e=>e instanceof Ge&&null!=e.assign&&e.assign instanceof Function}),(He=e.Rank||(e.Rank={})).R0="R0",He.R1="R1",He.R2="R2",He.R3="R3",He.R4="R4",He.R5="R5",He.R6="R6",function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(je||(je={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(Ze||(Ze={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Je||(Je={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(Ye||(Ye={}));const Xe={float32:Je,int32:je,bool:Ze,complex64:Ye};function Qe(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Xe[e][t]}function et(e,t){if(e.dtype===t.dtype)return[e,t];const n=Qe(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function tt(e,t){o(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function nt(e){const t=[];return function e(t,n,r){if(null==t)return;if(t instanceof Ge)return void n.push(t);if(s=t,!Array.isArray(s)&&"object"!=typeof s)return;var s;const o=t;for(const t in o){const s=o[t];r.has(s)||(r.add(s),e(s,n,r))}}(e,t,new Set),t}var rt=Object.freeze({__proto__:null,makeTypesMatch:et,assertTypesMatch:tt,isTensorInList:function(e,t){return t.some(t=>t.id===e.id)},getTensorsInContainer:nt});function st(e){return null!=e.kernelName}class ot{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class at{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ot}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then(()=>{});if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(e+" backend was already registered. Reusing existing backend factory."),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(null==this.registryFactory[e])throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Fe(this.backendInstance),!0}setupRegisteredKernels(){U(this.backendName).forEach(e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){U(e).forEach(t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])})}initializeBackend(e){const n=this.registryFactory[e];if(null==n)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const r=n.factory();if(!r||r instanceof t||"function"!=typeof r.then)return this.registry[e]=r,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,n=r.then(n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0)).catch(n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message)),!1));return this.pendingBackendInit=n,{success:n,asyncInit:!0}}}catch(t){return console.warn(`Initialization of backend ${e} failed`),console.warn(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(e+" backend not found in registry");this.backendName===e&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),o=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,o),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(e){throw t(),e}}nextTensorId(){return at.nextTensorId++}nextVariableId(){return at.nextVariableId++}clone(e){const t=lt.runKernel("Identity",{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],e=>({x:()=>{const t={x:e},n={dtype:"float32"};return lt.runKernel("Cast",t,n)}}),[],{}),t}runKernel(e,t,n){if(!(null!=K(e,this.backendName)))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let s=0;n.forEach(e=>{s+="complex64"===e.dtype?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=r-t-s-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;let i,l;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const u=st(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(st(e)){const{kernelName:t,inputs:s,attrs:a}=e;null==this.backendName&&this.backend;const u=K(t,this.backendName);o(null!=u,()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`),i=()=>{const e=this.backend.numDataIds();l=u.kernelFunc({inputs:s,attrs:a,backend:this.backend});const o=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const i=o.map(e=>{if(null!=e.rank)return e;const{dataId:t,shape:n,dtype:r}=e;return this.makeTensorFromDataId(t,n,r)});if(r){const e=this.getTensorsForGradient(t,s,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map(e=>this.keep(this.clone(e))))};i=()=>{const e=this.backend.numDataIds();l=this.tidy(()=>t(this.backend,s));const n=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,e,n),n}}const{inputs:c,attrs:h}=e,d=st(e)?null:e.backwardsFunc;let p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(p=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs):t=i()}),r&&this.addTapeNode(u,c,t,d,n,h),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(e=>null!=c[e]?c[e].shape:null),outputShapes:t.map(e=>e.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(e=>this.keep(this.clone(e)))}getTensorsForGradient(e,t,n){const r=W(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let a;r.saveAllInputs?(o(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(e=>t[e])):a=e.map(e=>t[e]);const i=n.filter((e,t)=>s[t]);return a.concat(i)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;"string"===n&&x(e[0])&&(s=e.map(e=>De(e)));const o=r.write(s,t,n),a=new Ge(t,n,o,this.nextTensorId());if(this.trackTensor(a,r),"string"===n){const e=this.state.tensorInfo.get(o),t=v(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return a}makeTensorFromDataId(e,t,n,r){const s=new Ge(t,n=n||"float32",e,this.nextTensorId());return this.trackTensor(s,r),s}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const s=new Ve(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[s.name])throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*k(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Ve||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*k(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(e=>e.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,s,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=W(e);null!=i&&(r=i.gradFunc),null!=r&&(a.gradient=e=>(e=e.map((e,t)=>{if(null==e){const e=n[t],r=T(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e}),r(e.length>1?e:e[0],s,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=nt(e),n=new Set(t.map(e=>e.id));for(let e=0;e<this.state.activeScope.track.length;e++){const t=this.state.activeScope.track[e];t.kept||n.has(t.id)||t.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(e=>{e.kept||e.scopeId!==r.id||this.track(e)})}gradients(e,t,n,r=!1){if(o(t.length>0,()=>"gradients() received an empty list of xs."),null!=n&&"float32"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));o(s instanceof Ge,()=>"The result y returned by f() must be a tensor.");const a=function(e,t,n){const r={},s={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){const o=e[n],a=o.inputs;for(const e in a){const n=a[e];let i=!1;for(let e=0;e<t.length;e++)if(r[n.id]){o.outputs.forEach(e=>r[e.id]=!0),i=!0,s[o.id]=!0;break}if(i)break}}const o={};o[n.id]=!0;const a={};for(let t=e.length-1;t>=0;t--){const n=e[t],r=n.inputs;for(let e=0;e<n.outputs.length;e++)if(o[n.outputs[e].id]){for(const e in r)o[r[e].id]=!0,a[n.id]=!0;break}}const i=[];for(let t=0;t<e.length;t++){const n=e[t];if(s[n.id]&&a[n.id]){const e={};for(const t in n.inputs){const s=n.inputs[t];r[s.id]&&(e[t]=s)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,i.push(t)}}return i}(this.state.activeTape,t,s);if(!r&&0===a.length&&t.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",()=>{const e={};e[s.id]=null==n?function(e){const t=$(u(e),"float32");return lt.makeTensor(t,e,"float32")}(s.shape):n,function(e,t,n,r){for(let s=t.length-1;s>=0;s--){const o=t[s],a=[];if(o.outputs.forEach(t=>{const n=e[t.id];null!=n?a.push(n):a.push(null)}),null==o.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${o.kernelName}.`);const i=o.gradient(a);for(const t in o.inputs){if(!(t in i))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(i)}.`);const s=n(()=>i[t]());if("float32"!==s.dtype)throw new Error(`Error in gradient for op ${o.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${s.dtype}'`);const a=o.inputs[t];if(!c(s.shape,a.shape))throw new Error(`Error in gradient for op ${o.kernelName}. The gradient of input '${t}' has shape '${s.shape}', which does not match the shape of the input '${a.shape}'`);if(null==e[a.id])e[a.id]=s;else{const t=e[a.id];e[a.id]=r(t,s),t.dispose()}}}}(e,a,e=>this.tidy(e),ut);const r=t.map(t=>e[t.id]);return 0===this.state.gradientDepth&&(this.state.activeTape.forEach(e=>{for(const t of e.saved)t.dispose()}),this.state.activeTape=null),{value:s,grads:r}})}customGrad(e){return o(_(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{let n;o(t.every(e=>e instanceof Ge),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");const r={};t.forEach((e,t)=>{r[t]=e});return this.runKernelFunc({forwardFunc:(r,s)=>(n=e(...t,s),o(n.value instanceof Ge,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),o(_(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),backwardsFunc:(e,r)=>{const s=n.gradFunc(e,r),a=Array.isArray(s)?s:[s];o(a.length===t.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(...)."),o(a.every(e=>e instanceof Ge),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const i={};return a.forEach((e,t)=>{i[t]=()=>e}),i},inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){const t=Te(),n=await this.backend.time(e);return n.wallMs=Te()-t,n}track(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ot;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}function it(){const t=L();if(null==t._tfengine){const e=new B(t);t._tfengine=new at(e)}var n;return n=t._tfengine.ENV,e.ENV=n,We=()=>t._tfengine,t._tfengine}at.nextTensorId=0,at.nextVariableId=0;const lt=it();function ut(e,t){const n={a:e,b:t};return lt.runKernel("Add",n)}function ct(){return"undefined"!=typeof window&&null!=window.document||"undefined"!=typeof WorkerGlobalScope}var ht=Object.freeze({__proto__:null,isMobile:function(e){if(e||"undefined"!=typeof navigator&&null!=navigator){if(e||(e=navigator),"ReactNative"===e.product)return!0;const t=e.userAgent||e.vendor||window.opera;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( 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Array.isArray(e)&&C().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&function e(t,n,r){if(r=r||[],!Array.isArray(t)&&!w(t))return void o(0===n.length,()=>`Element arr[${r.join("][")}] is a primitive, but should be an array/TypedArray of ${n[0]} elements`);o(n.length>0,()=>`Element arr[${r.join("][")}] should be a primitive, but is an array of ${t.length} elements`),o(t.length===n[0],()=>`Element arr[${r.join("][")}] should have ${n[0]} elements, but has ${t.length} elements`);const s=n.slice(1);for(let n=0;n<t.length;++n)e(t[n],s,r.concat(n))}(e,r,[]),r}function ft(e,t,n,r){if("string_or_numeric"!==e){if(null==e)throw new Error("Expected dtype cannot be null.");if("numeric"!==e&&e!==t||"numeric"===e&&"string"===t)throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function mt(e,t,n,r="numeric"){if(e instanceof Ge)return ft(r,e.dtype,t,n),e;let s=A(e);if("string"!==s&&["bool","int32","float32"].indexOf(r)>=0&&(s=r),ft(r,s,t,n),null==e||!w(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e){const r=null==e?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${r}'`)}const o=pt(e,s);w(e)||Array.isArray(e)||(e=[e]);const a="string"!==s?$e(e,s):l(e,[],!0);return lt.makeTensor(a,o,s)}function gt(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((e,s)=>mt(e,`${t}[${s}]`,n,r))}dt.registerFlag("DEBUG",()=>!1,e=>{e&&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.")}),dt.registerFlag("IS_BROWSER",()=>ct()),dt.registerFlag("IS_NODE",()=>"undefined"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node),dt.registerFlag("IS_CHROME",()=>"undefined"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),dt.registerFlag("PROD",()=>!1),dt.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>dt.getBool("DEBUG")),dt.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),dt.registerFlag("IS_TEST",()=>!1),dt.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0),dt.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function bt(e){const t=Object.keys(e);if(1!==t.length)throw new Error("Please provide an object with a single key (operation name) mapping to a function. Got an object with "+t.length+" keys.");let n=t[0];const r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n+="__op";const s=(...e)=>{lt.startScope(n);try{const t=r(...e);return R(t)&&console.error("Cannot return a Promise inside of tidy."),lt.endScope(t),t}catch(e){throw lt.endScope(null),e}};return Object.defineProperty(s,"name",{value:n,configurable:!0}),s}const yt=bt({complex_:function(e,t){const n=mt(e,"real","complex"),r=mt(t,"imag","complex");a(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);const s={real:n,imag:r};return lt.runKernel("Complex",s)}});function wt(e,t,n,r){if(null==r&&(r=A(e)),"complex64"===r)throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!w(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(null!=t){D(t);const e=u(t),r=u(n);o(e===r,()=>`Based on the provided shape, [${t}], the tensor should have ${e} values but has ${r}`);for(let e=0;e<n.length;++e){const r=n[e],s=e!==n.length-1||r!==u(t.slice(e));o(n[e]===t[e]||!s,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return w(e)||Array.isArray(e)||(e=[e]),t=t||n,e="string"!==r?$e(e,r):l(e,[],!0),lt.makeTensor(e,t,r)}function kt(e,t,n){return wt(e,t,pt(e,n),n)}const vt={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};function xt(e,t){const n={};let r,s=0;for(const o of t){const t=o.name,a=o.dtype,i=o.shape,l=u(i);let c;if("quantization"in o){const n=o.quantization;if("uint8"===n.dtype||"uint16"===n.dtype){if(!("min"in n)||!("scale"in n))throw new Error(`Weight ${o.name} with quantization ${n.dtype} doesn't have corresponding metadata min and scale.`)}else{if("float16"!==n.dtype)throw new Error(`Weight ${o.name} has unknown quantization dtype ${n.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);if("float32"!==a)throw new Error(`Weight ${o.name} is quantized with ${n.dtype} which only supports weights of type float32 not ${a}.`)}const i=vt[n.dtype],u=e.slice(s,s+l*i),h="uint8"===n.dtype?new Uint8Array(u):new Uint16Array(u);if("float32"===a)if("uint8"===n.dtype||"uint16"===n.dtype){c=new Float32Array(h.length);for(let e=0;e<h.length;e++){const t=h[e];c[e]=t*n.scale+n.min}}else{if("float16"!==n.dtype)throw new Error(`Unsupported quantization type ${n.dtype} for weight type float32.`);void 0===r&&(r=Mt()),c=r(h)}else{if("int32"!==a)throw new Error(`Unsupported dtype in weight '${t}': ${a}`);if("uint8"!==n.dtype&&"uint16"!==n.dtype)throw new Error(`Unsupported quantization type ${n.dtype} for weight type int32.`);c=new Int32Array(h.length);for(let e=0;e<h.length;e++){const t=h[e];c[e]=Math.round(t*n.scale+n.min)}}s+=l*i}else if("string"===a){const t=u(o.shape);c=[];for(let n=0;n<t;n++){const t=new Uint32Array(e.slice(s,s+4))[0];s+=4;const n=new Uint8Array(e.slice(s,s+t));c.push(n),s+=t}}else{const r=vt[a],o=e.slice(s,s+l*r);if("float32"===a)c=new Float32Array(o);else if("int32"===a)c=new Int32Array(o);else if("bool"===a)c=new Uint8Array(o);else{if("complex64"!==a)throw new Error(`Unsupported dtype in weight '${t}': ${a}`);{c=new Float32Array(o);const e=new Float32Array(c.length/2),r=new Float32Array(c.length/2);for(let t=0;t<e.length;t++)e[t]=c[2*t],r[t]=c[2*t+1];const s=kt(e,i,"float32"),a=kt(r,i,"float32");n[t]=yt(s,a),s.dispose(),a.dispose()}}s+=l*r}"complex64"!==a&&(n[t]=kt(c,i,a))}return n}function Et(e){if(null===e)throw new Error("Invalid input value: "+JSON.stringify(e));let t=0;const n=[];e.forEach(e=>{if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error("Unsupported TypedArray subtype: "+e.constructor.name)});const r=new Uint8Array(t);let s=0;return n.forEach(e=>{r.set(new Uint8Array(e.buffer),s),s+=e.byteLength}),r.buffer}const St="undefined"!=typeof Buffer&&("undefined"==typeof Blob||"undefined"==typeof atob||"undefined"==typeof btoa);function At(e){return St?Buffer.byteLength(e):new Blob([e]).size}function _t(e){if(1===e.length)return e[0];let t=0;e.forEach(e=>{t+=e.byteLength});const n=new Uint8Array(t);let r=0;return e.forEach(e=>{n.set(new Uint8Array(e),r),r+=e.byteLength}),n.buffer}function Nt(e){for(e=e.trim();e.endsWith("/");)e=e.slice(0,e.length-1);const t=e.split("/");return t[t.length-1]}function It(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==e.modelTopology?0:At(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:At(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}function Mt(){const e=function(){const e=e=>{let t=e<<13,n=0;for(;0==(8388608&t);)n-=8388608,t<<=1;return t&=-8388609,n+=947912704,t|n},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let e=1024;e<2048;e++)t[e]=939524096+(e-1024<<13);return t}(),t=function(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}(),n=function(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}();return r=>{const s=new ArrayBuffer(4*r.length),o=new Uint32Array(s);for(let s=0;s<r.length;s++){const a=r[s],i=e[n[a>>10]+(1023&a)]+t[a>>10];o[s]=i}return new Float32Array(s)}}class $t{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return null==$t.instance&&($t.instance=new $t),$t.instance}static registerSaveRouter(e){$t.getInstance().saveRouters.push(e)}static registerLoadRouter(e){$t.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return $t.getHandlers(e,"save")}static getLoadHandlers(e,t){return $t.getHandlers(e,"load",t)}static getHandlers(e,t,n){const r=[];return("load"===t?$t.getInstance().loadRouters:$t.getInstance().saveRouters).forEach(t=>{const s=t(e,n);null!==s&&r.push(s)}),r}}function Tt(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e="undefined"==typeof window?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Dt(e){const t=e.result;t.createObjectStore("models_store",{keyPath:"modelPath"}),t.createObjectStore("model_info_store",{keyPath:"modelPath"})}class Rt{constructor(e){if(this.indexedDB=Tt(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((e,n)=>{const r=this.indexedDB.open("tensorflowjs",1);r.onupgradeneeded=()=>Dt(r),r.onsuccess=()=>{const s=r.result;if(null==t){const t=s.transaction("models_store","readonly"),r=t.objectStore("models_store").get(this.modelPath);r.onsuccess=()=>{if(null==r.result)return s.close(),n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));e(r.result.modelArtifacts)},r.onerror=e=>(s.close(),n(r.error)),t.oncomplete=()=>s.close()}else{const r=It(t),o=s.transaction("model_info_store","readwrite");let a=o.objectStore("model_info_store");const i=a.put({modelPath:this.modelPath,modelArtifactsInfo:r});let l;i.onsuccess=()=>{l=s.transaction("models_store","readwrite");const i=l.objectStore("models_store").put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:r});i.onsuccess=()=>e({modelArtifactsInfo:r}),i.onerror=e=>{a=o.objectStore("model_info_store");const t=a.delete(this.modelPath);t.onsuccess=()=>(s.close(),n(i.error)),t.onerror=e=>(s.close(),n(i.error))}},i.onerror=e=>(s.close(),n(i.error)),o.oncomplete=()=>{null==l?s.close():l.oncomplete=()=>s.close()}}},r.onerror=e=>n(r.error)})}}Rt.URL_SCHEME="indexeddb://";const Bt=e=>{return C().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Rt.URL_SCHEME)?(t=e.slice(Rt.URL_SCHEME.length),new Rt(t)):null;var t};$t.registerSaveRouter(Bt),$t.registerLoadRouter(Bt);class Ft{constructor(){this.indexedDB=Tt()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open("tensorflowjs",1);n.onupgradeneeded=()=>Dt(n),n.onsuccess=()=>{const r=n.result,s=r.transaction("model_info_store","readonly"),o=s.objectStore("model_info_store").getAll();o.onsuccess=()=>{const t={};for(const e of o.result)t[e.modelPath]=e.modelArtifactsInfo;e(t)},o.onerror=e=>(r.close(),t(o.error)),s.oncomplete=()=>r.close()},n.onerror=e=>t(n.error)})}async removeModel(e){var t;return e=(t=e).startsWith(Rt.URL_SCHEME)?t.slice(Rt.URL_SCHEME.length):t,new Promise((t,n)=>{const r=this.indexedDB.open("tensorflowjs",1);r.onupgradeneeded=()=>Dt(r),r.onsuccess=()=>{const s=r.result,o=s.transaction("model_info_store","readwrite"),a=o.objectStore("model_info_store"),i=a.get(e);let l;i.onsuccess=()=>{if(null==i.result)return s.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const r=a.delete(e),o=()=>{l=s.transaction("models_store","readwrite");const r=l.objectStore("models_store").delete(e);r.onsuccess=()=>t(i.result.modelArtifactsInfo),r.onerror=e=>n(i.error)};r.onsuccess=o,r.onerror=e=>(o(),s.close(),n(i.error))}},i.onerror=e=>(s.close(),n(i.error)),o.oncomplete=()=>{null==l?s.close():l.oncomplete=()=>s.close()}},r.onerror=e=>n(r.error)})}}const Ct="tensorflowjs_models",Pt="info",Lt="model_topology",Ot="weight_specs",zt="weight_data",qt="model_metadata";function Kt(e){return{info:[Ct,e,Pt].join("/"),topology:[Ct,e,Lt].join("/"),weightSpecs:[Ct,e,Ot].join("/"),weightData:[Ct,e,zt].join("/"),modelMetadata:[Ct,e,qt].join("/")}}function Wt(e){const t=e.split("/");if(t.length<3)throw new Error("Invalid key format: "+e);return t.slice(1,t.length-1).join("/")}class Ut{constructor(e){if(!C().getBool("IS_BROWSER")||"undefined"==typeof window||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Kt(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=It(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,function(e){if(St)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let e=0,r=t.length;e<r;e++)n+=String.fromCharCode(t[e]);return btoa(n)}(e.weightData));const s={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return null!=e.signature&&(s.signature=e.signature),null!=e.userDefinedMetadata&&(s.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(s.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(s)),{modelArtifactsInfo:r}}catch(e){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(null==e)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if("JSON"!==e.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(null==n)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(null==r)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;const s=this.LS.getItem(this.keys.modelMetadata);if(null!=s){const e=JSON.parse(s);t.format=e.format,t.generatedBy=e.generatedBy,t.convertedBy=e.convertedBy,null!=e.signature&&(t.signature=e.signature),null!=e.userDefinedMetadata&&(t.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(t.modelInitializer=e.modelInitializer)}const o=this.LS.getItem(this.keys.weightData);if(null==o)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=function(e){if(St){const t=Buffer.from(e,"base64");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let e=0;e<t.length;++e)n.set([t.charCodeAt(e)],e);return n.buffer}(o),t}}Ut.URL_SCHEME="localstorage://";const Gt=e=>{return C().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Ut.URL_SCHEME)?(t=e.slice(Ut.URL_SCHEME.length),new Ut(t)):null;var t};$t.registerSaveRouter(Gt),$t.registerLoadRouter(Gt);class Vt{constructor(){o(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),o("undefined"==typeof window||void 0!==window.localStorage,()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Ct+"/",n="/"+Pt;for(let r=0;r<this.LS.length;++r){const s=this.LS.key(r);if(s.startsWith(t)&&s.endsWith(n)){e[Wt(s)]=JSON.parse(this.LS.getItem(s))}}return e}async removeModel(e){var t;const n=Kt(e=(t=e).startsWith(Ut.URL_SCHEME)?t.slice(Ut.URL_SCHEME.length):t);if(null==this.LS.getItem(n.info))throw new Error(`Cannot find model at path '${e}'`);const r=JSON.parse(this.LS.getItem(n.info));return this.LS.removeItem(n.info),this.LS.removeItem(n.topology),this.LS.removeItem(n.weightSpecs),this.LS.removeItem(n.weightData),r}}class Ht{constructor(){this.managers={}}static getInstance(){return null==Ht.instance&&(Ht.instance=new Ht),Ht.instance}static registerManager(e,t){o(null!=e,()=>"scheme must not be undefined or null."),e.endsWith("://")&&(e=e.slice(0,e.indexOf("://"))),o(e.length>0,()=>"scheme must not be an empty string.");const n=Ht.getInstance();o(null==n.managers[e],()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(null==t)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function jt(e){if(-1===e.indexOf("://"))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+Ht.getSchemes().join(","));return{scheme:e.split("://")[0],path:e.split("://")[1]}}async function Zt(e,t,n=!1){o(e!==t,()=>`Old path and new path are the same: '${e}'`);const r=$t.getLoadHandlers(e);o(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),o(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);const s=r[0],a=$t.getSaveHandlers(t);o(a.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),o(a.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);const i=a[0],l=jt(e).scheme,u=jt(e).path,c=l===jt(e).scheme,h=await s.load();n&&c&&await Ht.getManager(l).removeModel(u);const d=await i.save(h);return n&&!c&&await Ht.getManager(l).removeModel(u),d.modelArtifactsInfo}class Jt{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if("utf-8"!==t&&"utf8"!==t)throw new Error("Browser's encoder only supports utf-8, but got "+t);return null==this.textEncoder&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new Jt);try{Ht.registerManager(Ut.URL_SCHEME,new Vt)}catch(e){}try{Ht.registerManager(Rt.URL_SCHEME,new Ft)}catch(e){}}const Yt=()=>require("node-fetch");let Xt;class Qt{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return null!=C().global.fetch?C().global.fetch(e,t):(null==Xt&&(Xt=Yt()),Xt(e,t))}now(){const e=process.hrtime();return 1e3*e[0]+e[1]/1e6}encode(e,t){if("utf-8"!==t&&"utf8"!==t)throw new Error("Node built-in encoder only supports utf-8, but got "+t);return this.textEncoder.encode(e)}decode(e,t){return 0===e.length?"":new this.util.TextDecoder(t).decode(e)}}function en(e,t="float32",n){return t=t||"float32",D(e),new Ke(e,t,n)}C().get("IS_NODE")&&C().setPlatform("node",new Qt);const tn=bt({cast_:function(e,t){const n=mt(e,"x","cast");if(!y(t))throw new Error("Failed to cast to unknown dtype "+t);if("string"===t&&"string"!==n.dtype||"string"!==t&&"string"===n.dtype)throw new Error("Only strings can be casted to strings");const r={x:n},s={dtype:t};return lt.runKernel("Cast",r,s)}});const nn=bt({clone_:function(e){const t={x:mt(e,"x","clone","string_or_numeric")};return lt.runKernel("Identity",t)}});function rn(e,t=!1){console.log(e.toString(t))}it();Ue={buffer:en,cast:tn,clone:nn,print:rn};function sn(e){return new Promise(e=>setTimeout(e)).then(e)}class on{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(on.URL_SCHEME)&&(e=e.slice(on.URL_SCHEME.length)),null!=e&&0!==e.length||(e="model"),this.modelTopologyFileName=e+".json",this.weightDataFileName=e+".weights.bin"}async save(e){if("undefined"==typeof document)throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};null!=e.signature&&(r.signature=e.signature),null!=e.userDefinedMetadata&&(r.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(r.modelInitializer=e.modelInitializer);const s=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),o=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=s,await sn(()=>o.dispatchEvent(new MouseEvent("click"))),null!=e.weightData){const e=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor;e.download=this.weightDataFileName,e.href=t,await sn(()=>e.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:It(e)}}}}on.URL_SCHEME="downloads://";class an{constructor(e){if(null==e||e.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+e);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{const s=new FileReader;s.onload=s=>{const o=JSON.parse(s.target.result),a=o.modelTopology;if(null==a)return void r(new Error("modelTopology field is missing from file "+e.name));0===t.length&&n({modelTopology:a});const i=o.weightsManifest;if(null==i)return void r(new Error("weightManifest field is missing from file "+e.name));let l;try{l=this.checkManifestAndWeightFiles(i,t)}catch(e){return void r(e)}const u=[],c=[],h=[];i.forEach(e=>{e.paths.forEach(e=>{c.push(e),h.push(null)}),u.push(...e.weights)}),i.forEach(e=>{e.paths.forEach(e=>{const t=new FileReader;t.onload=t=>{const r=t.target.result,s=c.indexOf(e);if(h[s]=r,-1===h.indexOf(null)){const e={modelTopology:a,weightSpecs:u,weightData:_t(h),format:o.format,generatedBy:o.generatedBy,convertedBy:o.convertedBy};null!=o.signature&&(e.signature=o.signature),null!=o.userDefinedMetadata&&(e.userDefinedMetadata=o.userDefinedMetadata),null!=o.modelInitializer&&(e.modelInitializer=o.modelInitializer),n(e)}},t.onerror=t=>r(`Failed to weights data from file of path '${e}'.`),t.readAsArrayBuffer(l[e])})})},s.onerror=t=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),s.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],r=t.map(e=>Nt(e.name)),s={};for(const o of e)o.paths.forEach(e=>{const o=Nt(e);if(-1!==n.indexOf(o))throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),-1===r.indexOf(o))throw new Error(`Weight file with basename '${o}' is not provided.`);s[e]=t[r.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return s}}function ln(e,t,n,r){!function(e){o(null!=e&&Array.isArray(e)&&e.length>0,()=>"promises must be a none empty array")}(e),function(e,t){o(e>=0&&e<=1,()=>"Progress fraction must be in range [0, 1], but got startFraction "+e),o(t>=0&&t<=1,()=>"Progress fraction must be in range [0, 1], but got endFraction "+t),o(t>=e,()=>`startFraction must be no more than endFraction, but got startFraction ${e} and endFraction `+t)}(n=null==n?0:n,r=null==r?1:r);let s=0;return Promise.all(e.map(o=>(o.then(o=>{const a=n+ ++s/e.length*(r-n);return t(a),o}),o)))}async function un(e,t){null==t&&(t={});const n=null==t.fetchFunc?C().platform.fetch:t.fetchFunc,r=e.map(e=>n(e,t.requestInit,{isBinary:!0})),s=(null==t.onProgress?await Promise.all(r):await ln(r,t.onProgress,0,.5)).map(e=>e.arrayBuffer());return null==t.onProgress?await Promise.all(s):await ln(s,t.onProgress,.5,1)}function cn(e){return async(t,n="",r)=>{const s=t.map(()=>!1),o={},a=null!=r?r.map(()=>!1):[],i=[];if(t.forEach((e,t)=>{let n=0;e.weights.forEach(e=>{const l="quantization"in e?e.quantization.dtype:e.dtype,c=vt[l]*u(e.shape),h=()=>{s[t]=!0,null==o[t]&&(o[t]=[]),o[t].push({manifestEntry:e,groupOffset:n,sizeBytes:c})};null!=r?r.forEach((t,n)=>{t===e.name&&(h(),a[n]=!0)}):h(),i.push(e.name),n+=c})}),!a.every(e=>e)){const e=r.filter((e,t)=>!a[t]);throw new Error("Could not find weights in manifest with names: "+e.join(", ")+". \nManifest JSON has weights with names: "+i.join(", ")+".")}const l=s.reduce((e,t,n)=>(t&&e.push(n),e),[]),c=[];l.forEach(e=>{t[e].paths.forEach(e=>{const t=n+(n.endsWith("/")?"":"/")+e;c.push(t)})});const h=await e(c),d={};let p=0;return l.forEach(e=>{const n=t[e].paths.length;let r=0;for(let e=0;e<n;e++)r+=h[p+e].byteLength;const s=new ArrayBuffer(r),a=new Uint8Array(s);let i=0;for(let e=0;e<n;e++){const t=new Uint8Array(h[p+e]);a.set(t,i),i+=t.byteLength}o[e].forEach(e=>{const t=xt(s.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(const e in t)d[e]=t[e]}),p+=n}),d}}$t.registerSaveRouter(e=>C().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(on.URL_SCHEME)?function(e="model"){return new on(e)}(e.slice(on.URL_SCHEME.length)):null);class hn{constructor(e,t){if(this.DEFAULT_METHOD="POST",null==t&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,null!=t.fetchFunc?(o("function"==typeof t.fetchFunc,()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=C().platform.fetch,o(null!=e&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&o(2===e.length,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,null!=t.requestInit&&null!=t.requestInit.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;const n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};null!=e.signature&&(r.signature=e.signature),null!=e.userDefinedMetadata&&(r.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=e.weightData&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:"application/octet-stream"}),"model.weights.bin");const s=await this.fetch(this.path,t);if(s.ok)return{modelArtifactsInfo:It(e),responses:[s]};throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+s.status+".")}async load(){const e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code `+e.status+". Please verify this URL points to the model JSON of the model to load.");let t;try{t=await e.json()}catch(e){let t=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?t+=" 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.":t+=" Please make sure the server is serving valid JSON for this request.",new Error(t)}const n=t.modelTopology,r=t.weightsManifest,s=t.generatedBy,o=t.convertedBy,a=t.format,i=t.signature,l=t.userDefinedMetadata;if(null==n&&null==r)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let u,c;if(null!=r){const e=await this.loadWeights(r);[u,c]=e}const h={modelTopology:n,weightSpecs:u,weightData:c,generatedBy:s,convertedBy:o,format:a};null!=i&&(h.signature=i),null!=l&&(h.userDefinedMetadata=l);const d=t.modelInitializer;return d&&(h.modelInitializer=d),h}async loadWeights(e){const t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=function(e){const t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),s=n>t?e.substring(n):"";return[r+"/",s]}(t),s=this.weightPathPrefix||n,o=[];for(const t of e)o.push(...t.weights);const a=[],i=[];for(const t of e)for(const e of t.paths)null!=this.weightUrlConverter?i.push(this.weightUrlConverter(e)):a.push(s+e+r);this.weightUrlConverter&&a.push(...await Promise.all(i));return[o,_t(await un(a,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress}))]}}function dn(e){return null!=e.match(hn.URL_SCHEME_REGEX)}hn.URL_SCHEME_REGEX=/^https?:\/\//;const pn=(e,t)=>{if("undefined"==typeof fetch&&(null==t||null==t.fetchFunc))return null;{let n=!0;if(n=Array.isArray(e)?e.every(e=>dn(e)):dn(e),n)return fn(e,t)}return null};function fn(e,t){return new hn(e,t)}$t.registerSaveRouter(pn),$t.registerLoadRouter(pn);class mn{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}}class gn{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}}var bn=Object.freeze({__proto__:null,browserFiles:function(e){return new an(e)},browserHTTPRequest:function(e,t){return fn(e,t)},concatenateArrayBuffers:_t,decodeWeights:xt,encodeWeights:async function(e,t){const n=[],r=[],s=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);for(let o=0;o<s.length;++o){const a=s[o],i=Array.isArray(e)?e[o].tensor:e[a];if("float32"!==i.dtype&&"int32"!==i.dtype&&"bool"!==i.dtype&&"string"!==i.dtype&&"complex64"!==i.dtype)throw new Error(`Unsupported dtype in weight '${a}': ${i.dtype}`);const l={name:a,shape:i.shape,dtype:i.dtype};if("string"===i.dtype){const e=new Promise(async e=>{const t=await i.bytes(),n=t.reduce((e,t)=>e+t.length,0)+4*t.length,r=new Uint8Array(n);let s=0;for(let e=0;e<t.length;e++){const n=t[e],o=new Uint8Array(new Uint32Array([n.length]).buffer);r.set(o,s),s+=4,r.set(n,s),s+=n.length}e(r)});r.push(e)}else r.push(i.data());null!=t&&(l.group=t),n.push(l)}return{data:Et(await Promise.all(r)),specs:n}},fromMemory:function(e,t,n,r){if(1===arguments.length){return null!=e.modelTopology||null!=e.weightSpecs?new mn(e):(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 mn({modelTopology:e}))}return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. 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t={x:mt(e,"x","atanh")};return lt.runKernel("Atanh",t)}});function vr(e,t,n,r,s,o,a="channelsLast"){const[i,l]=Ar(t);let u;if("channelsLast"===a)u=[i,l,e[3],e[3]];else{if("channelsFirst"!==a)throw new Error("Unknown dataFormat "+a);u=[i,l,e[1],e[1]]}return xr(e,u,n,r,s,o,!1,a)}function xr(e,t,n,r,s,o,a=!1,i="channelsLast"){let[l,u,c,h]=[-1,-1,-1,-1];if("channelsLast"===i)[l,u,c,h]=e;else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);[l,h,u,c]=e}const[d,p,,f]=t,[m,g]=Ar(n),[b,y]=Ar(r),w=Nr(d,b),k=Nr(p,y),{padInfo:v,outHeight:x,outWidth:E}=function(e,t,n,r,s,o,a,i,l){let u,c,h;if("number"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};const s=function(e,t,n,r,s){null==r&&(r=Sr(e,t,n));const o=e[0],a=e[1],i=Ir((o-t+2*r)/n+1,s),l=Ir((a-t+2*r)/n+1,s);return[i,l]}([t,n],o,r,e,i);c=s[0],h=s[1]}else if("same"===e){c=Math.ceil(t/r),h=Math.ceil(n/s);const e=Math.max(0,(c-1)*r+o-t),i=Math.max(0,(h-1)*s+a-n),l=Math.floor(e/2),d=e-l,p=Math.floor(i/2);u={top:l,bottom:d,left:p,right:i-p,type:"SAME"}}else if("valid"===e)u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-o+1)/r),h=Math.ceil((n-a+1)/s);else{if("object"!=typeof e)throw Error("Unknown padding parameter: "+e);{const d="channelsLast"===l?e[1][0]:e[2][0],p="channelsLast"===l?e[1][1]:e[2][1],f="channelsLast"===l?e[2][0]:e[3][0],m="channelsLast"===l?e[2][1]:e[3][1];u={top:d,bottom:p,left:f,right:m,type:0===d&&0===p&&0===f&&0===m?"VALID":"EXPLICIT"},c=Ir((t-o+d+p)/r+1,i),h=Ir((n-a+f+m)/s+1,i)}}return{padInfo:u,outHeight:c,outWidth:h}}(s,u,c,m,g,w,k,o,i),S=a?f*h:f;let A;return"channelsFirst"===i?A=[l,S,x,E]:"channelsLast"===i&&(A=[l,x,E,S]),{batchSize:l,dataFormat:i,inHeight:u,inWidth:c,inChannels:h,outHeight:x,outWidth:E,outChannels:S,padInfo:v,strideHeight:m,strideWidth:g,filterHeight:d,filterWidth:p,effectiveFilterHeight:w,effectiveFilterWidth:k,dilationHeight:b,dilationWidth:y,inShape:e,outShape:A,filterShape:t}}function Er(e,t,n,r,s,o=!1,a="channelsLast",i){let[l,u,c,h,d]=[-1,-1,-1,-1,-1];if("channelsLast"===a)[l,u,c,h,d]=e;else{if("channelsFirst"!==a)throw new Error("Unknown dataFormat "+a);[l,d,u,c,h]=e}const[p,f,m,,g]=t,[b,y,w]=_r(n),[k,v,x]=_r(r),E=Nr(p,k),S=Nr(f,v),A=Nr(m,x),{padInfo:_,outDepth:N,outHeight:I,outWidth:M}=function(e,t,n,r,s,o,a,i,l,u,c){let h,d,p,f;if("number"==typeof e){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:0===e?"VALID":"NUMBER"};const o=function(e,t,n,r,s,o){null==s&&(s=Sr(e,t,r));const a=e[0],i=e[1],l=e[2],u=Ir((a-t+2*s)/r+1,o),c=Ir((i-t+2*s)/r+1,o),h=Ir((l-t+2*s)/r+1,o);return[u,c,h,n]}([t,n,r,1],i,1,s,e,c);d=o[0],p=o[1],f=o[2]}else if("same"===e){d=Math.ceil(t/s),p=Math.ceil(n/o),f=Math.ceil(r/a);const e=(d-1)*s+i-t,c=(p-1)*o+l-n,m=(f-1)*a+u-r,g=Math.floor(e/2),b=e-g,y=Math.floor(c/2),w=c-y,k=Math.floor(m/2);h={top:y,bottom:w,left:k,right:m-k,front:g,back:b,type:"SAME"}}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-i+1)/s),p=Math.ceil((n-l+1)/o),f=Math.ceil((r-u+1)/a)}return{padInfo:h,outDepth:d,outHeight:p,outWidth:f}}(s,u,c,h,b,y,w,E,S,A,i),$=o?g*d:g;let T;return"channelsFirst"===a?T=[l,$,N,I,M]:"channelsLast"===a&&(T=[l,N,I,M,$]),{batchSize:l,dataFormat:a,inDepth:u,inHeight:c,inWidth:h,inChannels:d,outDepth:N,outHeight:I,outWidth:M,outChannels:$,padInfo:_,strideDepth:b,strideHeight:y,strideWidth:w,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:E,effectiveFilterHeight:S,effectiveFilterWidth:A,dilationDepth:k,dilationHeight:v,dilationWidth:x,inShape:e,outShape:T,filterShape:t}}function Sr(e,t,n,r=1){const s=Nr(t,r);return Math.floor((e[0]*(n-1)-n+s)/2)}function Ar(e){return"number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function _r(e){return"number"==typeof e?[e,e,e]:e}function Nr(e,t){return t<=1?e:e+(e-1)*(t-1)}function Ir(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error("Unknown roundingMode "+t)}}function Mr(e){const[t,n,r]=Ar(e);return 1===t&&1===n&&1===r}function $r(e,t){return Mr(e)||Mr(t)}function Tr(e){if("NHWC"===e)return"channelsLast";if("NCHW"===e)return"channelsFirst";throw new Error("Unknown dataFormat "+e)}const Dr=bt({reshape_:function(e,t){const n={x:mt(e,"x","reshape","string_or_numeric")},r={shape:t};return lt.runKernel("Reshape",n,r)}});const Rr=bt({avgPool_:function(e,t,n,r,s){const a=mt(e,"x","avgPool","float32");o($r(n,1),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '1'`);let i=a,l=!1;3===a.rank&&(l=!0,i=Dr(a,[1,a.shape[0],a.shape[1],a.shape[2]])),o(4===i.rank,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),null!=s&&o(h(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);const u={x:i},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s};let d=lt.runKernel("AvgPool",u,c);return d=tn(d,a.dtype),l?Dr(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});const Br=bt({avgPool3d_:function(e,t,n,r,s,a="NDHWC"){const i=mt(e,"x","avgPool3d","float32");let l=i,u=!1;4===i.rank&&(u=!0,l=Dr(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),o(5===l.rank,()=>`Error in avgPool3d: x must be rank 5 but got rank ${l.rank}.`),o("NDHWC"===a,()=>"Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of "+a),null!=s&&o(h(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${s} but got pad ${r}.`);const c={x:l},d={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a};let p=lt.runKernel("AvgPool3D",c,d);return p=tn(p,l.dtype),u?Dr(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});const Fr=bt({concat_:function(e,t=0){o(e.length>=1,()=>"Pass at least one tensor to concat");const n=gt(e,"tensors","concat","string_or_numeric");if("complex64"===n[0].dtype&&n.forEach(e=>{if("complex64"!==e.dtype)throw new Error(`Cannot concatenate complex64 tensors with a tensor\n with dtype ${e.dtype}. `)}),1===n.length)return nn(n[0]);const r=n,s={axis:t};return lt.runKernel("Concat",r,s)}});const Cr=bt({sigmoid_:function(e){const t={x:mt(e,"x","sigmoid")};return lt.runKernel("Sigmoid",t)}});const Pr=bt({slice_:function(e,t,n){const r=mt(e,"x","slice","string_or_numeric");if(0===r.rank)throw new Error("Slicing scalar is not possible");const s={x:r},o={begin:t,size:n};return lt.runKernel("Slice",s,o)}});const Lr=bt({tanh_:function(e){const t={x:mt(e,"x","tanh")};return lt.runKernel("Tanh",t)}});const Or=bt({basicLSTMCell_:function(e,t,n,r,s,o){const a=mt(e,"forgetBias","basicLSTMCell"),i=mt(t,"lstmKernel","basicLSTMCell"),l=mt(n,"lstmBias","basicLSTMCell"),u=mt(r,"data","basicLSTMCell"),c=mt(s,"c","basicLSTMCell"),h=mt(o,"h","basicLSTMCell"),d=Fr([u,h],1),p=yn(d,i),f=sr(p,l),m=f.shape[0],g=f.shape[1]/4,b=[m,g],y=Pr(f,[0,0],b),w=Pr(f,[0,g],b),k=Pr(f,[0,2*g],b),v=Pr(f,[0,3*g],b),x=sr(ir(Cr(y),Lr(w)),ir(c,Cr(sr(a,k))));return[x,ir(Lr(x),Cr(v))]}});const zr=bt({batchToSpaceND_:function(e,t,n){const r=mt(e,"x","batchToSpaceND"),s=t.reduce((e,t)=>e*t);o(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),o(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),o(r.shape[0]%s==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${s}`);const a={x:r},i={blockShape:t,crops:n};return lt.runKernel("BatchToSpaceND",a,i)}});const qr=bt({batchNorm_:function(e,t,n,r,s,a){null==a&&(a=.001);const i=mt(e,"x","batchNorm"),l=mt(t,"mean","batchNorm"),u=mt(n,"variance","batchNorm");let c,h;null!=s&&(c=mt(s,"scale","batchNorm")),null!=r&&(h=mt(r,"offset","batchNorm")),o(l.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),o(null==h||l.rank===h.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),o(null==c||l.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const d={x:function(e){let t;return t=0===e.rank||1===e.rank?Dr(e,[1,1,1,e.size]):2===e.rank?Dr(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?Dr(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}(i),scale:c,offset:h,mean:l,variance:u},p={varianceEpsilon:a},f=lt.runKernel("FusedBatchNorm",d,p);return Dr(f,i.shape)}});const Kr=bt({batchNorm2d_:function(e,t,n,r,s,a){const i=mt(e,"x","batchNorm"),l=mt(t,"mean","batchNorm"),u=mt(n,"variance","batchNorm");let c,h;return null!=s&&(c=mt(s,"scale","batchNorm")),null!=r&&(h=mt(r,"offset","batchNorm")),o(2===i.rank,()=>"Error in batchNorm2D: x must be rank 2 but got rank "+i.rank+"."),o(2===l.rank||1===l.rank,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${l.rank}.`),o(2===u.rank||1===u.rank,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),null!=c&&o(2===c.rank||1===c.rank,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),null!=h&&o(2===h.rank||1===h.rank,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${h.rank}.`),qr(i,l,u,h,c,a)}});const Wr=bt({batchNorm3d_:function(e,t,n,r,s,a){const i=mt(e,"x","batchNorm"),l=mt(t,"mean","batchNorm"),u=mt(n,"variance","batchNorm");let c,h;return null!=s&&(c=mt(s,"scale","batchNorm")),null!=r&&(h=mt(r,"offset","batchNorm")),o(3===i.rank,()=>"Error in batchNorm3D: x must be rank 3 but got rank "+i.rank+"."),o(3===l.rank||1===l.rank,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${l.rank}.`),o(3===u.rank||1===u.rank,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),null!=c&&o(3===c.rank||1===c.rank,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),null!=h&&o(3===h.rank||1===h.rank,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${h.rank}.`),qr(i,l,u,h,c,a)}});const Ur=bt({batchNorm4d_:function(e,t,n,r,s,a){const i=mt(e,"x","batchNorm"),l=mt(t,"mean","batchNorm"),u=mt(n,"variance","batchNorm");let c,h;return null!=s&&(c=mt(s,"scale","batchNorm")),null!=r&&(h=mt(r,"offset","batchNorm")),o(4===i.rank,()=>"Error in batchNorm4D: x must be rank 4 but got rank "+i.rank+"."),o(4===l.rank||1===l.rank,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${l.rank}.`),o(4===u.rank||1===u.rank,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),null!=c&&o(4===c.rank||1===c.rank,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),null!=h&&o(4===h.rank||1===h.rank,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${h.rank}.`),qr(i,l,u,h,c,a)}});const Gr=bt({bincount_:function(e,t,n){const r=mt(e,"x","bincount"),s=mt(t,"weights","bincount");o("int32"===r.dtype,()=>"Error in bincount: input dtype must be int32, but got "+r.dtype),o(n>=0,()=>`size must be non-negative, but got ${n}.`),o(s.size===r.size||0===s.size,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: `+s.shape+".");const a={x:r,weights:s},i={size:n};return lt.runKernel("Bincount",a,i)}});const Vr=bt({broadcastTo_:function(e,t){let n=mt(e,"broadcastTo","x");const r=n.shape;if(t.some(e=>!(e>0)||e%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const e=n.shape.slice();for(;e.length<t.length;)e.unshift(1);n=Dr(n,e)}const s=n.shape,o=Array.from(t);for(let e=t.length-1;e>=0;e--)if(s[e]===t[e])o[e]=1;else if(1!==n.shape[e])throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(0===o.map((e,t)=>e>1?t:-1).filter(e=>e>=0).length)return nn(n);const a={x:n},i={reps:o};return lt.runKernel("Tile",a,i)}});const Hr=bt({ceil_:function(e){const t={x:mt(e,"x","ceil")};return lt.runKernel("Ceil",t)}});const jr=bt({clipByValue_:function(e,t,n){const r=mt(e,"x","clipByValue");o(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);const s={x:r},a={clipValueMin:t,clipValueMax:n};return lt.runKernel("ClipByValue",s,a)}});const Zr=bt({concat1d_:function(e){return Fr(e,0)}});const Jr=bt({concat2d_:function(e,t){return Fr(e,t)}});const Yr=bt({concat3d_:function(e,t){return Fr(e,t)}});const Xr=bt({concat4d_:function(e,t){return Fr(e,t)}});const Qr=bt({conv2d_:function(e,t,n,r,s="NHWC",a=[1,1],i){const l=mt(e,"x","conv2d"),u=mt(t,"filter","conv2d");let c=l,d=!1;3===l.rank&&(d=!0,c=Dr(l,[1,l.shape[0],l.shape[1],l.shape[2]])),o(4===c.rank,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),o(4===u.rank,()=>"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=i&&o(h(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);const p="NHWC"===s?c.shape[3]:c.shape[1];o(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),o($r(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);const f={x:c,filter:u},m={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:i},g=lt.runKernel("Conv2D",f,m);return d?Dr(g,[g.shape[1],g.shape[2],g.shape[3]]):g}});const es=bt({conv1d_:function(e,t,n,r,s="NWC",a=1,i){const l=mt(e,"x","conv1d"),u=mt(t,"filter","conv1d");let c=l,d=!1;2===l.rank&&(d=!0,c=Dr(l,[1,l.shape[0],l.shape[1]])),o(3===c.rank,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),o(3===u.rank,()=>"Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."),null!=i&&o(h(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),o(c.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${u.shape[1]}.`),o($r(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),o("NWC"===s,()=>`Error in conv1d: got dataFormat of ${s} but only NWC is currently supported.`);const p=Dr(u,[1,u.shape[0],u.shape[1],u.shape[2]]),f=Dr(c,[c.shape[0],1,c.shape[1],c.shape[2]]),m=Qr(f,p,[1,n],r,"NHWC",[1,a],i);return Dr(m,d?[m.shape[2],m.shape[3]]:[m.shape[0],m.shape[2],m.shape[3]])}});const ts=bt({conv2DBackpropInput_:function(e,t,n,r,s,a="NHWC",i){o(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let l=e,u=t,c=!1;3===t.rank&&(c=!0,u=Dr(t,[1,t.shape[0],t.shape[1],t.shape[2]]),l=[1,e[0],e[1],e[2]]),o(4===l.length,()=>"Error in conv2dDerInput: inShape must be length 4, but got length "+l.length+"."),o(4===u.rank,()=>"Error in conv2dDerInput: dy must be rank 4, but got rank "+u.rank),o(4===n.rank,()=>"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank);const d="NHWC"===a?l[3]:l[1],p="NHWC"===a?u.shape[3]:u.shape[1];o(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`),o(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),null!=i&&o(h(s),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);const f={dy:u,filter:n},m={strides:r,pad:s,dataFormat:a,dimRoundingMode:i,inputShape:l},g=lt.runKernel("Conv2DBackpropInput",f,m);return c?Dr(g,[g.shape[1],g.shape[2],g.shape[3]]):g}});const ns=bt({conv2dTranspose_:function(e,t,n,r,s,o){const a=mt(e,"x","conv2dTranspose"),i=mt(t,"filter","conv2dTranspose");return ts(n,a,i,r,s,"NHWC",o)}});const rs=bt({conv3d_:function(e,t,n,r,s="NDHWC",a=[1,1,1]){const i=mt(e,"x","conv3d"),l=mt(t,"filter","conv3d");let u=i,c=!1;4===i.rank&&(c=!0,u=Dr(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),o(5===u.rank,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),o(5===l.rank,()=>"Error in conv3d: filter must be rank 5, but got rank "+l.rank+"."),o(u.shape[4]===l.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${l.shape[3]}.`),o($r(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o("NDHWC"===s,()=>`Error in conv3d: got dataFormat of ${s} but only NDHWC is currently supported.`);const h={x:u,filter:l},d={strides:n,pad:r,dataFormat:s,dilations:a},p=lt.runKernel("Conv3D",h,d);return c?Dr(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});const ss=bt({conv3DBackpropInput_:function(e,t,n,r,s){o(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,i=t,l=!1;4===t.rank&&(l=!0,i=Dr(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);const u=a[4],c=i.shape[4];o(5===a.length,()=>"Error in conv3dDerInput: inShape must be length 5, but got length "+a.length+"."),o(5===i.rank,()=>"Error in conv3dDerInput: dy must be rank 5, but got rank "+i.rank),o(5===n.rank,()=>"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),o(u===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`),o(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);const h={dy:i,filter:n},d={pad:s,strides:r,inputShape:a},p=lt.runKernel("Conv3DBackpropInputV2",h,d);return l?Dr(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});const os=bt({conv3dTranspose_:function(e,t,n,r,s){const o=mt(e,"x","conv3dTranspose"),a=mt(t,"filter","conv3dTranspose");return ss(n,o,a,r,s)}});const as=bt({cos_:function(e){const t={x:mt(e,"x","cos")};return lt.runKernel("Cos",t)}});const is=bt({cosh_:function(e){const t={x:mt(e,"x","cosh")};return lt.runKernel("Cosh",t)}});const ls=bt({cumsum_:function(e,t=0,n=!1,r=!1){const s={x:mt(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:r};return lt.runKernel("Cumsum",s,o)}});const us=bt({denseBincount_:function(e,t,n,r=!1){const s=mt(e,"x","denseBincount"),a=mt(t,"weights","denseBincount");o("int32"===s.dtype,()=>"Error in denseBincount: input dtype must be int32, but got "+s.dtype),o(s.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${s.rank}.`),o(n>=0,()=>`size must be non-negative, but got ${n}.`),o(a.size===s.size||0===a.size,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${s.shape}, weights shape: `+a.shape+".");const i={x:s,weights:a},l={size:n,binaryOutput:r};return lt.runKernel("DenseBincount",i,l)}});const cs=bt({depthToSpace_:function(e,t,n="NHWC"){const r=mt(e,"x","depthToSpace"),s="NHWC"===n?r.shape[1]:r.shape[2],a="NHWC"===n?r.shape[2]:r.shape[3],i="NHWC"===n?r.shape[3]:r.shape[1];o(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${s} and ${t} for depthToSpace with input shape\n ${r.shape}`),o(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${a} and ${t} for depthToSpace with input shape\n ${r.shape}`),o(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);const l={x:r},u={blockSize:t,dataFormat:n};return lt.runKernel("DepthToSpace",l,u)}});const hs=bt({depthwiseConv2d_:function(e,t,n,r,s="NHWC",a=[1,1],i){const l=mt(e,"x","depthwiseConv2d"),u=mt(t,"filter","depthwiseConv2d");let c=l,d=!1;3===l.rank&&(d=!0,c=Dr(l,[1,l.shape[0],l.shape[1],l.shape[2]])),o(4===c.rank,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),o(4===u.rank,()=>"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),o(c.shape[3]===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),null!=i&&o(h(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);const p={x:c,filter:u},f={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:i},m=lt.runKernel("DepthwiseConv2dNative",p,f);return d?Dr(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const ds=bt({diag_:function(e){const t={x:mt(e,"x","diag")};return lt.runKernel("Diag",t)}});const ps=bt({dilation2d_:function(e,t,n,r,s=[1,1],a="NHWC"){const i=mt(e,"x","dilation2d"),l=mt(t,"filter","dilation2d");o(3===i.rank||4===i.rank,()=>"Error in dilation2d: input must be rank 3 or 4, but got rank "+i.rank+"."),o(3===l.rank,()=>"Error in dilation2d: filter must be rank 3, but got rank "+l.rank+"."),o("NHWC"===a,()=>"Error in dilation2d: Only NHWC is currently supported, but got dataFormat of "+a);let u=i,c=!1;3===i.rank&&(u=Dr(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);const h={x:u,filter:l},d={strides:n,pad:r,dilations:s},p=lt.runKernel("Dilation2D",h,d);return c?Dr(p,[p.shape[1],p.shape[2],p.shape[3]]):p}});function fs(e,t){const n=[];for(let r=0;r<t.length;r++){const s=e[e.length-r-1],o=t.length-r-1,a=t[o];(null==s||1===s&&a>1)&&n.unshift(o)}return n}function ms(e,t){const n=[],r=Math.max(e.length,t.length);for(let s=0;s<r;s++){let r=e[e.length-s-1];null==r&&(r=1);let o=t[t.length-s-1];if(null==o&&(o=1),1===r)n.unshift(o);else if(1===o)n.unshift(r);else{if(r!==o){throw Error(`Operands could not be broadcast together with shapes ${e} and ${t}.`)}n.unshift(r)}}return n}const gs=bt({equal_:function(e,t){let n=mt(e,"a","equal","string_or_numeric"),r=mt(t,"b","equal","string_or_numeric");[n,r]=et(n,r),ms(n.shape,r.shape);const s={a:n,b:r};return lt.runKernel("Equal",s)}});const bs=bt({where_:function(e,t,n){const r=mt(t,"a","where"),s=mt(n,"b","where"),o=mt(e,"condition","where","bool"),a=ms(ms(o.shape,r.shape),s.shape),i={condition:Vr(o,a),t:Vr(r,a),e:Vr(s,a)};return lt.runKernel("Select",i)}});const ys=bt({zerosLike_:function(e){const t={x:mt(e,"x","zerosLike")};return lt.runKernel("ZerosLike",t)}});const ws=bt({divNoNan_:function(e,t){let n=mt(e,"a","div"),r=mt(t,"b","div");[n,r]=et(n,r);const s=ar(n,r),o=ys(s),a=gs(r,o);return bs(a,o,s)}});const ks=bt({dot_:function(e,t){const n=mt(e,"t1","dot"),r=mt(t,"t2","dot");o(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);const s=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];if(o(s===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${s} and ${a}.`),1===n.rank&&1===r.rank){const e=Dr(n,[1,-1]),t=Dr(r,[-1,1]),s=yn(e,t);return Dr(s,[])}if(1===n.rank&&2===r.rank){const e=Dr(n,[1,-1]),t=Dr(r,[r.shape[0],r.shape[1]]),s=yn(e,t);return Dr(s,[s.size])}if(2===n.rank&&1===r.rank){const e=Dr(r,[-1,1]),t=yn(n,e);return Dr(t,[t.size])}{const e=Dr(r,[r.shape[0],r.shape[1]]);return yn(n,e)}}});const vs=bt({einsum_:function(e,...t){const n=t.map((e,t)=>mt(e,"tensors"+t,"einsum")),r={equation:e};return lt.runKernel("Einsum",n,r)}});const xs=bt({elu_:function(e){const t={x:mt(e,"x","elu")};return lt.runKernel("Elu",t)}});const Es=bt({erf_:function(e){let t=mt(e,"x","erf");o("int32"===t.dtype||"float32"===t.dtype,()=>"Input dtype must be `int32` or `float32`."),"int32"===t.dtype&&(t=tn(t,"float32"));const n={x:t};return lt.runKernel("Erf",n)}});const Ss=bt({exp_:function(e){const t={x:mt(e,"x","exp")};return lt.runKernel("Exp",t)}});const As=bt({expandDims_:function(e,t=0){const n=mt(e,"x","expandDims","string_or_numeric");o(t<=n.rank,()=>"Axis must be <= rank of the tensor");const r={input:n},s={dim:t};return lt.runKernel("ExpandDims",r,s)}});const _s=bt({expm1_:function(e){const t={x:mt(e,"x","expm1")};return lt.runKernel("Expm1",t)}});const Ns=bt({tile_:function(e,t){const n=mt(e,"x","tile","string_or_numeric");o(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);const r={x:n},s={reps:t};return lt.runKernel("Tile",r,s)}});const Is=bt({eye_:function(e,t,n,r="float32"){null==t&&(t=e);const s=en([e,t],r),o=e<=t?e:t;for(let e=0;e<o;++e)s.set(1,e,e);const a=Dr(s.toTensor(),[e,t]);if(null==n)return a;if(1===n.length)return Ns(As(a,0),[n[0],1,1]);if(2===n.length)return Ns(As(As(a,0),0),[n[0],n[1],1,1]);if(3===n.length)return Ns(As(As(As(a,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}});function Ms(e,t,n){const r={shape:e,value:t,dtype:n};return lt.runKernel("Fill",{},r)}const $s=bt({floor_:function(e){const t={x:mt(e,"x","floor")};return lt.runKernel("Floor",t)}});const Ts=bt({gather_:function(e,t,n=0,r=0){const s={x:mt(e,"x","gather"),indices:mt(t,"indices","gather","int32")},o={axis:n,batchDims:r};return lt.runKernel("GatherV2",s,o)}});const Ds=bt({greater_:function(e,t){let n=mt(e,"a","greater","string_or_numeric"),r=mt(t,"b","greater","string_or_numeric");[n,r]=et(n,r),ms(n.shape,r.shape);const s={a:n,b:r};return lt.runKernel("Greater",s)}});const Rs=bt({greaterEqual_:function(e,t){let n=mt(e,"a","greaterEqual","string_or_numeric"),r=mt(t,"b","greaterEqual","string_or_numeric");[n,r]=et(n,r),ms(n.shape,r.shape);const s={a:n,b:r};return lt.runKernel("GreaterEqual",s)}});const Bs=bt({imag_:function(e){const t={input:mt(e,"input","imag")};return lt.runKernel("Imag",t)}});const Fs=bt({isFinite_:function(e){const t={x:mt(e,"x","isFinite")};return lt.runKernel("IsFinite",t)}});const Cs=bt({isInf_:function(e){const t={x:mt(e,"x","isInf")};return lt.runKernel("IsInf",t)}});const Ps=bt({isNaN_:function(e){const t={x:mt(e,"x","isNaN")};return lt.runKernel("IsNan",t)}});const Ls=bt({leakyRelu_:function(e,t=.2){const n={x:mt(e,"x","leakyRelu")},r={alpha:t};return lt.runKernel("LeakyRelu",n,r)}});const Os=bt({less_:function(e,t){let n=mt(e,"a","less","string_or_numeric"),r=mt(t,"b","less","string_or_numeric");[n,r]=et(n,r),ms(n.shape,r.shape);const s={a:n,b:r};return lt.runKernel("Less",s)}});const zs=bt({lessEqual_:function(e,t){let n=mt(e,"a","lessEqual","string_or_numeric"),r=mt(t,"b","lessEqual","string_or_numeric");[n,r]=et(n,r),ms(n.shape,r.shape);const s={a:n,b:r};return lt.runKernel("LessEqual",s)}});const qs=bt({localResponseNormalization_:function(e,t=5,n=1,r=1,s=.5){const a=mt(e,"x","localResponseNormalization");o(4===a.rank||3===a.rank,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank ${a.rank}.`),o(h(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=a,l=!1;3===a.rank&&(l=!0,i=Dr(a,[1,a.shape[0],a.shape[1],a.shape[2]]));const u={x:i},c={depthRadius:t,bias:n,alpha:r,beta:s},d=lt.runKernel("LRN",u,c);return l?Dr(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});const Ks=bt({log_:function(e){const t={x:mt(e,"x","log")};return lt.runKernel("Log",t)}});const Ws=bt({log1p_:function(e){const t={x:mt(e,"x","log1p")};return lt.runKernel("Log1p",t)}});function Us(e,t){o(_(e),()=>"The f passed in variableGrads(f) must be a 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Please make sure the operations that use variables are inside the function f passed to minimize()."),o(0===a.rank,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);const l={};return t.forEach((e,t)=>{null!=i[t]&&(l[e.name]=i[t])}),null!=r&&r.forEach(e=>l[e.name]=null),{value:a,grads:l}}function Gs(e){return lt.customGrad(e)}function Vs(e){if(e.filter(e=>null==e).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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a=mt(e,"boxes","nonMaxSuppression"),i=mt(t,"scores","nonMaxSuppression"),l=yi(a,i,n,r,s,null),u={boxes:a,scores:i},c={maxOutputSize:l.maxOutputSize,iouThreshold:l.iouThreshold,scoreThreshold:l.scoreThreshold,padToMaxOutputSize:o},h=lt.runKernel("NonMaxSuppressionV4",u,c);return{selectedIndices:h[0],validOutputs:h[1]}}});const Ri=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,o=!1){const a=mt(e,"boxes","nonMaxSuppressionAsync"),i=mt(t,"scores","nonMaxSuppressionAsync"),l=yi(a,i,n,r,s,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,[d,p]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:m}=Ei(d,p,u,c,h,o);return a!==e&&a.dispose(),i!==t&&i.dispose(),{selectedIndices:Fa(f,"int32"),validOutputs:ha(m,"int32")}};const Bi=bt({resizeBilinear_:function(e,t,n=!1,r=!1){const s=mt(e,"images","resizeBilinear");o(3===s.rank||4===s.rank,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`),o(2===t.length,()=>"Error in resizeBilinear: new 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p=Hs(ar(yn(c,h),o)),f=Pr(a,[e,0],[n-e,r]),m=ir(p,l),g=kn(l);if(0===e)a=Ys(f,yn(m,yn(g,f)));else{const t=Ys(f,yn(m,yn(g,f)));a=Fr([Pr(a,[0,0],[e,r]),t],0)}const b=kn(m),y=Pr(s,[0,e],[n,s.shape[1]-e]);if(0===e)s=Ys(y,yn(yn(y,l),b));else{const t=Ys(y,yn(yn(y,l),b));s=Fr([Pr(s,[0,0],[n,e]),t],1)}return[l,a,s]}),nr([t,o,u])}return!t&&n>r&&(s=Pr(s,[0,0],[n,r]),a=Pr(a,[0,0],[r,r])),[s,a]})}const qi=bt({qr_:function(e,t=!1){if(o(e.rank>=2,()=>"qr() requires input tensor to have a rank >= 2, but got rank "+e.rank),2===e.rank)return zi(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((e,t)=>e*t),r=qa(Dr(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),s=[],o=[];r.forEach(e=>{const[n,r]=zi(e,t);s.push(n),o.push(r)});return[Dr(Ta(s,0),e.shape),Dr(Ta(o,0),e.shape)]}}});var Ki;(Ki=e.Reduction||(e.Reduction={}))[Ki.NONE=0]="NONE",Ki[Ki.MEAN=1]="MEAN",Ki[Ki.SUM=2]="SUM",Ki[Ki.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";const Wi=bt({computeWeightedLoss_:function(t,n,r=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=mt(t,"losses","computeWeightedLoss");let o=null;null!=n&&(o=mt(n,"weights","computeWeightedLoss"));const a=null==o?s:ir(s,o);if(r===e.Reduction.NONE)return a;if(r===e.Reduction.SUM)return Xs(a);if(r===e.Reduction.MEAN){if(null==o)return po(a);{const e=s.size/o.size,t=ar(Xs(a),Xs(o));return e>1?ar(t,ha(e)):t}}if(r===e.Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return ar(Xs(a),ha(s.size));{const e=ir(o,mo(s.shape)),t=tn(Xs(So(e,ha(0))),"float32");return ar(Xs(a),t)}}throw Error("Unknown reduction: "+r)}});const Ui=bt({absoluteDifference_:function(t,n,r,s=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=mt(t,"labels","absoluteDifference"),i=mt(n,"predictions","absoluteDifference");let l=null;null!=r&&(l=mt(r,"weights","absoluteDifference")),a(o.shape,i.shape,"Error in absoluteDifference: ");const u=lr(Ys(o,i));return Wi(u,l,s)}});const Gi=bt({cosineDistance_:function(t,n,r,s,o=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=mt(t,"labels","cosineDistance"),l=mt(n,"predictions","cosineDistance");let u=null;null!=s&&(u=mt(s,"weights","cosineDistance")),a(i.shape,l.shape,"Error in cosineDistance: ");const c=ha(1),h=Ys(c,Xs(ir(i,l),r,!0));return Wi(h,u,o)}});const Vi=bt({hingeLoss_:function(t,n,r,s=e.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=mt(t,"labels","hingeLoss");const i=mt(n,"predictions","hingeLoss");let l=null;null!=r&&(l=mt(r,"weights","hingeLoss")),a(o.shape,i.shape,"Error in hingeLoss: ");const u=ha(1);o=Ys(ir(ha(2),o),u);const c=na(Ys(u,ir(o,i)));return Wi(c,l,s)}});const Hi=bt({huberLoss_:function(t,n,r,s=1,o=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=mt(t,"labels","huberLoss"),l=mt(n,"predictions","huberLoss");let u=null;null!=r&&(u=mt(r,"weights","huberLoss")),a(i.shape,l.shape,"Error in huberLoss: ");const c=ha(s),h=lr(Ys(l,i)),d=bo(h,c),p=Ys(h,d),f=sr(ir(ha(.5),ko(d)),ir(c,p));return Wi(f,u,o)}});const ji=bt({logLoss_:function(t,n,r,s=1e-7,o=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=mt(t,"labels","logLoss"),l=mt(n,"predictions","logLoss");let u=null;null!=r&&(u=mt(r,"weights","logLoss")),a(i.shape,l.shape,"Error in logLoss: ");const c=ha(1),h=ha(s),d=Hs(ir(i,Ks(sr(l,h)))),p=ir(Ys(c,i),Ks(sr(Ys(c,l),h))),f=Ys(d,p);return Wi(f,u,o)}});const Zi=bt({meanSquaredError_:function(t,n,r,s=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=mt(t,"labels","meanSquaredError"),i=mt(n,"predictions","meanSquaredError");let l=null;null!=r&&(l=mt(r,"weights","meanSquaredError")),a(o.shape,i.shape,"Error in meanSquaredError: ");const u=Ma(o,i);return Wi(u,l,s)}});const Ji=bt({sigmoidCrossEntropy_:function(t,n,r,s=0,o=e.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=mt(t,"multiClassLabels","sigmoidCrossEntropy");const l=mt(n,"logits","sigmoidCrossEntropy");let u=null;if(null!=r&&(u=mt(r,"weights","sigmoidCrossEntropy")),a(i.shape,l.shape,"Error in sigmoidCrossEntropy: "),s>0){const e=ha(s),t=ha(1),n=ha(.5);i=sr(ir(i,Ys(t,e)),ir(n,e))}const c=function(e,t){const n=mt(e,"labels","sigmoidCrossEntropyWithLogits"),r=mt(t,"logits","sigmoidCrossEntropyWithLogits");a(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");const s=na(r),o=ir(r,n),i=Ws(Ss(Hs(lr(r))));return sr(Ys(s,o),i)}(i,l);return Wi(c,u,o)}});const Yi=bt({softmaxCrossEntropy_:function(t,n,r,s=0,o=e.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=mt(t,"onehotLabels","softmaxCrossEntropy");const l=mt(n,"logits","softmaxCrossEntropy");let u=null;if(null!=r&&(u=mt(r,"weights","softmaxCrossEntropy")),a(i.shape,l.shape,"Error in softmaxCrossEntropy: "),s>0){const e=ha(s),t=ha(1),n=ha(i.shape[1]);i=sr(ir(i,Ys(t,e)),ar(e,n))}const c=function(e,t,n=-1){if(-1===n&&(n=t.rank-1),n!==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 `+n);return Gs((e,t,r)=>{const s=ro(t,[n],!0),o=Ys(tn(t,"float32"),s);r([e,o]);const a=Hs(ir(o,e));return{value:Xs(a,[n]),gradFunc:(e,t)=>{const[r,s]=t,o=no(e.shape,[n]);return[ir(Dr(e,o),Ys(tn(r,"float32"),Ss(s))),ir(Dr(e,o),Ys(Ss(s),tn(r,"float32")))]}}})(e,t)}(i,l);return Wi(c,u,o)}});const Xi=bt({sparseFillEmptyRows_:function(e,t,n,r){const s=mt(e,"indices","sparseFillEmptyRows"),o=mt(t,"values","sparseFillEmptyRows"),a=mt(n,"denseShape","sparseFillEmptyRows"),i=mt(r,"defaultValue","sparseFillEmptyRows",o.dtype);if(2!==s.rank)throw new Error("Indices should be Tensor2D but received shape\n "+s.shape);if(1!==o.rank)throw new Error("Values should be Tensor1D but received shape "+o.shape);if(1!==a.rank)throw new Error("Dense shape should be Tensor1D but received shape "+a.shape);if(0!==i.rank)throw new Error("Default value should be a scalar but received shape "+i.shape);const l={indices:s,values:o,denseShape:a,defaultValue:i},u=lt.runKernel("SparseFillEmptyRows",l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}});const Qi=bt({sparseReshape_:function(e,t,n){const r=mt(e,"inputIndices","sparseReshape"),s=mt(t,"inputShape","sparseReshape"),o=mt(n,"newShape","sparseReshape");if(2!==r.rank)throw new Error("Input indices should be Tensor2D but received shape\n "+r.shape);if(1!==s.rank)throw new Error("Input shape should be Tensor1D but received shape "+s.shape);if(1!==o.rank)throw new Error("New shape should be Tensor1D but received shape "+o.shape);const a={inputIndices:r,inputShape:s,newShape:o},i=lt.runKernel("SparseReshape",a);return{outputIndices:i[0],outputShape:i[1]}}});const el=bt({sparseSegmentMean_:function(e,t,n){const r=mt(e,"data","sparseSegmentMean"),s=mt(t,"indices","sparseSegmentMean"),o=mt(n,"segmentIds","sparseSegmentMean");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==s.rank)throw new Error("Indices should be Tensor1D but received shape\n "+s.shape);if(1!==o.rank)throw new Error("Segment ids should be Tensor1D but received shape\n "+o.shape);const a={data:r,indices:s,segmentIds:o};return lt.runKernel("SparseSegmentMean",a)}});const tl=bt({sparseSegmentSum_:function(e,t,n){const r=mt(e,"data","sparseSegmentSum"),s=mt(t,"indices","sparseSegmentSum"),o=mt(n,"segmentIds","sparseSegmentSum");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==s.rank)throw new Error("Indices should be Tensor1D but received shape\n "+s.shape);if(1!==o.rank)throw new Error("Segment ids should be Tensor1D but received shape\n "+o.shape);const a={data:r,indices:s,segmentIds:o};return lt.runKernel("SparseSegmentSum",a)}});const nl=bt({stringNGrams_:function(e,t,n,r,s,o,a,i){const l=mt(e,"data","stringNGrams","string");if("string"!==l.dtype)throw new Error("Data must be of datatype string");if(1!==l.shape.length)throw new Error("Data must be a vector, saw: "+l.shape);const u=mt(t,"dataSplits","stringNGrams");if("int32"!==u.dtype)throw new Error("Data splits must be of datatype int32");const c={separator:n,nGramWidths:r,leftPad:s,rightPad:o,padWidth:a,preserveShortSequences:i},h={data:l,dataSplits:u},d=lt.runKernel("StringNGrams",h,c);return{nGrams:d[0],nGramsSplits:d[1]}}});const rl=bt({stringSplit_:function(e,t,n=!0){const r=mt(e,"input","stringSplit","string"),s=mt(t,"delimiter","stringSplit","string");if(1!==r.rank)throw new Error("Input should be Tensor1D but received shape "+r.shape);if(0!==s.rank)throw new Error("Delimiter should be a scalar but received shape "+s.shape);const o={skipEmpty:n},a={input:r,delimiter:s},i=lt.runKernel("StringSplit",a,o);return{indices:i[0],values:i[1],shape:i[2]}}});const sl=bt({stringToHashBucketFast_:function(e,t){const n=mt(e,"input","stringToHashBucketFast","string"),r={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");const s={input:n};return lt.runKernel("StringToHashBucketFast",s,r)}}),ol={fft:Ea,ifft:Sa,rfft:Na,irfft:Aa},al={hammingWindow:hi,hannWindow:di,frame:pi,stft:fi},il={flipLeftRight:gi,resizeNearestNeighbor:Fi,resizeBilinear:Bi,rotateWithOffset:bi,cropAndResize:mi,nonMaxSuppression:wi,nonMaxSuppressionAsync:Mi,nonMaxSuppressionWithScore:$i,nonMaxSuppressionWithScoreAsync:Ti,nonMaxSuppressionPadded:Di,nonMaxSuppressionPaddedAsync:Ri,threshold:Ci,transform:Pi},ll={bandPart:Li,gramSchmidt:Oi,qr:qi},ul={absoluteDifference:Ui,computeWeightedLoss:Wi,cosineDistance:Gi,hingeLoss:Vi,huberLoss:Hi,logLoss:ji,meanSquaredError:Zi,sigmoidCrossEntropy:Ji,softmaxCrossEntropy:Yi},cl={sparseFillEmptyRows:Xi,sparseReshape:Qi,sparseSegmentMean:el,sparseSegmentSum:tl},hl={stringNGrams:nl,stringSplit:rl,stringToHashBucketFast:sl};class dl extends Hn{minimize(e,t=!1,n){const{value:r,grads:s}=this.computeGradients(e,n);if(null!=n){const e=n.map(e=>({name:e.name,tensor:s[e.name]}));this.applyGradients(e)}else this.applyGradients(s);return nr(s),t?r:(r.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Us(e,t)}dispose(){null!=this.iterations_&&nr(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:ha(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for this optimizer class "+this.getClassName())}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(dl,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class pl extends dl{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=lt.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=lt.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:t+"/accum_grad",variable:tr(()=>ys(r).variable(!1))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:t+"/accum_var",variable:tr(()=>ys(r).variable(!1))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedGrads[n].variable,a=this.accumulatedUpdates[n].variable;tr(()=>{const e=sr(ir(o,this.rho),ir(ko(s),1-this.rho)),t=ir(ar(Ia(sr(a,this.epsilon)),Ia(sr(o,this.epsilon))),s),n=sr(ir(a,this.rho),ir(ko(t),1-this.rho));o.assign(e),a.assign(n);const i=sr(ir(t,-this.learningRate),r);r.assign(i)})}),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(nr(this.accumulatedGrads.map(e=>e.variable)),nr(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)})),this.accumulatedUpdates=e.slice(t,2*t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}pl.className="Adadelta",Zn(pl);class fl extends dl{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=lt.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:t+"/accumulator",variable:tr(()=>Ms(r.shape,this.initialAccumulatorValue).variable(e))}}const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedGrads[n].variable;tr(()=>{const e=sr(o,ko(s));o.assign(e);const t=sr(ir(ar(s,Ia(sr(e,lt.backend.epsilon()))),-this.learningRate),r);r.assign(t)})}),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&nr(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.map(e=>({originalName:e.name,variable:e.tensor.variable(!1)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}fl.className="Adagrad",Zn(fl);class ml extends dl{constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tr(()=>{this.accBeta1=ha(t).variable(),this.accBeta2=ha(n).variable()}),null==r&&(this.epsilon=lt.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);tr(()=>{const n=Ys(1,this.accBeta1),r=Ys(1,this.accBeta2);t.forEach((t,s)=>{const o=lt.registeredVariables[t];null==this.accumulatedFirstMoment[s]&&(this.accumulatedFirstMoment[s]={originalName:t+"/m",variable:tr(()=>ys(o).variable(!1))}),null==this.accumulatedSecondMoment[s]&&(this.accumulatedSecondMoment[s]={originalName:t+"/v",variable:tr(()=>ys(o).variable(!1))});const a=Array.isArray(e)?e[s].tensor:e[t];if(null==a)return;const i=this.accumulatedFirstMoment[s].variable,l=this.accumulatedSecondMoment[s].variable,u=sr(ir(i,this.beta1),ir(a,1-this.beta1)),c=sr(ir(l,this.beta2),ir(ko(a),1-this.beta2)),h=ar(u,n),d=ar(c,r);i.assign(u),l.assign(c);const p=sr(ir(ar(h,sr(Ia(d),this.epsilon)),-this.learningRate),o);o.assign(p)}),this.accBeta1.assign(ir(this.accBeta1,this.beta1)),this.accBeta2.assign(ir(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&nr(this.accumulatedFirstMoment.map(e=>e.variable)),null!=this.accumulatedSecondMoment&&nr(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e),tr(()=>{this.accBeta1.assign(Bo(this.beta1,this.iterations_+1)),this.accBeta2.assign(Bo(this.beta2,this.iterations_+1))});const t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)})),this.accumulatedSecondMoment=e.slice(t,2*t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}ml.className="Adam",Zn(ml);class gl extends dl{constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tr(()=>{this.iteration=ha(0).variable(),this.accBeta1=ha(t).variable()}),null==r&&(this.epsilon=lt.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);tr(()=>{const n=Ys(1,this.accBeta1),r=ar(-this.learningRate,sr(ir(this.iteration,this.decay),1));t.forEach((t,s)=>{const o=lt.registeredVariables[t];null==this.accumulatedFirstMoment[s]&&(this.accumulatedFirstMoment[s]={originalName:t+"/m",variable:ys(o).variable(!1)}),null==this.accumulatedWeightedInfNorm[s]&&(this.accumulatedWeightedInfNorm[s]={originalName:t+"/v",variable:ys(o).variable(!1)});const a=Array.isArray(e)?e[s].tensor:e[t];if(null==a)return;const i=this.accumulatedFirstMoment[s].variable,l=this.accumulatedWeightedInfNorm[s].variable,u=sr(ir(i,this.beta1),ir(a,1-this.beta1)),c=ir(l,this.beta2),h=lr(a),d=ho(c,h);i.assign(u),l.assign(d);const p=sr(ir(ar(r,n),ar(u,sr(d,this.epsilon))),o);o.assign(p)}),this.iteration.assign(sr(this.iteration,1)),this.accBeta1.assign(ir(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&nr(this.accumulatedFirstMoment.map(e=>e.variable)),null!=this.accumulatedWeightedInfNorm&&nr(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}gl.className="Adamax",Zn(gl);class bl extends dl{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const s=lt.registeredVariables[t];tr(()=>{const e=sr(ir(this.c,r),s);s.assign(e)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=rr(ha(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}bl.className="SGD",Zn(bl);class yl extends bl{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ha(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=lt.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:t+"/momentum",variable:tr(()=>ys(r).variable(e))}}const s=this.accumulations[n].variable,o=Array.isArray(e)?e[n].tensor:e[t];null!=o&&tr(()=>{let e;const t=sr(ir(this.m,s),o);e=this.useNesterov?sr(ir(this.c,sr(o,ir(t,this.m))),r):sr(ir(this.c,t),r),s.assign(t),r.assign(e)})}),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&nr(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);this.accumulations=e.map(e=>({originalName:e.name,variable:e.tensor.variable(!1)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}yl.className="Momentum",Zn(yl);class wl extends dl{constructor(e,t=.9,n=0,r=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,null==r&&(this.epsilon=lt.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=lt.registeredVariables[t];null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:t+"/rms",variable:tr(()=>ys(r).variable(!1))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:t+"/momentum",variable:tr(()=>ys(r).variable(!1))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:t+"/mg",variable:tr(()=>ys(r).variable(!1))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedMeanSquares[n].variable,a=this.accumulatedMoments[n].variable;tr(()=>{const e=sr(ir(o,this.decay),ir(ko(s),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,i=sr(ir(t,this.decay),ir(s,1-this.decay)),l=ar(ir(s,this.learningRate),Ia(Ys(e,sr(ko(i),this.epsilon)))),u=sr(ir(a,this.momentum),l);o.assign(e),t.assign(i),a.assign(u);const c=Ys(r,u);r.assign(c)}else{const e=sr(ir(o,this.decay),ir(ko(s),1-this.decay)),t=sr(ir(a,this.momentum),ar(ir(s,this.learningRate),Ia(sr(e,this.epsilon))));o.assign(e),a.assign(t);const n=Ys(r,t);r.assign(n)}})}),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&nr(this.accumulatedMeanSquares.map(e=>e.variable)),null!=this.accumulatedMeanGrads&&this.centered&&nr(this.accumulatedMeanGrads.map(e=>e.variable)),null!=this.accumulatedMoments&&nr(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2;this.accumulatedMeanSquares=e.slice(0,t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)})),this.accumulatedMoments=e.slice(t,2*t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)})),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map(e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}wl.className="RMSProp",Zn(wl);class kl{static sgd(e){return new bl(e)}static momentum(e,t,n=!1){return new yl(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,s=!1){return new wl(e,t,n,r,s)}static adam(e=.001,t=.9,n=.999,r=null){return new ml(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new pl(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,s=0){return new gl(e,t,n,r,s)}static adagrad(e,t=.1){return new fl(e,t)}}const vl={sgd:kl.sgd,momentum:kl.momentum,adadelta:kl.adadelta,adagrad:kl.adagrad,rmsprop:kl.rmsprop,adamax:kl.adamax,adam:kl.adam},xl="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:"undefined"!=typeof setImmediate?setImmediate:e=>e();const El=/->/g;function Sl(e,t){const n=[];for(let r=0;r<e.length;++r)0!==e[r].length&&-1===e[r].indexOf(t)&&-1!==t||n.push(r);return n}var Al=Object.freeze({__proto__:null,segOpComputeOptimalWindowSize:function(e,t){let n,r=!1;for(e<=30?(n=e,r=!0):n=N(e,Math.floor(Math.sqrt(e)));!r;)n>t||n===e?r=!0:n=N(e,n+1);return n},computeOutShape:function(e,t,n){const r=[],s=e.length;for(let o=0;o<s;o++)o!==t?r.push(e[o]):r.push(n);return r},collectGatherOpShapeInfo:function(e,t,n,r){const s=t.shape.length,o=e.shape.length;if(0!==r&&(r<-s||r>s))throw new Error(`Expect batchDims in the range of [-${s}, ${s}], but got ${r}`);if(r<0&&(r+=s),r>o)throw new Error(`batchDims (${r}) must be less than rank(x) (\n ${o}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let n=0;n<r;++n)if(e.shape[n]!==t.shape[n])throw new Error(`x.shape[${n}]: ${e.shape[n]} should be equal to indices.shape[${n}]: ${t.shape[n]}.`);const a=e.shape[n],i=[];let l=1,u=1,c=1;for(let t=0;t<r;++t)i.push(e.shape[t]),l*=e.shape[t];for(let t=r;t<n;t++)i.push(e.shape[t]),u*=e.shape[t];for(let e=r;e<s;e++)i.push(t.shape[e]);for(let t=n+1;t<o;t++)i.push(e.shape[t]),c*=e.shape[t];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:a,outputShape:i}}});var _l=Object.freeze({__proto__:null,slice_util:Vn,segment_util:Al,fromUint8ToStringArray:function(e){try{return e.map(e=>Re(e))}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8, error: "+e)}},fromStringArrayToUint8:function(e){return e.map(e=>De(e))},upcastType:Qe,axesAreInnerMostDims:eo,combineLocations:to,computeOutAndReduceShapes:function(e,t){const n=[],r=e.length;for(let s=0;s<r;s++)-1===t.indexOf(s)&&n.push(e[s]);return[n,t.map(t=>e[t])]},expandShapeToKeepDim:no,assertAxesAreInnerMostDims:function(e,t,n){o(eo(t,n),()=>e+" supports only inner-most axes for now. 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the rest (${r}) along the non-concatenated axis ${s}.`)})},computeOutShape:function(e,t){const n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n},computeDilation2DInfo:function(e,t,n,r,s="NHWC",o){return xr(e,[...t,e[3]],n,o,r,null,null,Tr(s))},computePool2DInfo:vr,computePool3DInfo:function(e,t,n,r,s,o,a="NDHWC"){const[i,l,u]=_r(t);let c,h;if("NDHWC"===a)h="channelsLast",c=[i,l,u,e[4],e[4]];else{if("NCDHW"!==a)throw new Error("Unknown dataFormat "+a);h="channelsFirst",c=[i,l,u,e[1],e[1]]}return Er(e,c,n,r,s,!1,h,o)},computeConv2DInfo:xr,computeConv3DInfo:Er,computeDefaultPad:Sr,tupleValuesAreOne:Mr,eitherStridesOrDilationsAreOne:$r,convertConv2DDataFormat:Tr,getFusedDyActivation:ti,getFusedBiasGradient:ni,applyActivation:ri,shouldFuse:si,PARALLELIZE_THRESHOLD:30,computeOptimalWindowSize:function(e){return e<=30?e:N(e,Math.floor(Math.sqrt(e)))},getImageCenter:function(e,t,n){return[n*("number"==typeof e?e:e[0]),t*("number"==typeof 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r},prepareAndValidate:Mn,validateUpdateShape:Tn,validateInput:Dn,calculateShapes:Rn,SELU_SCALEALPHA:1.7580993408473768,SELU_SCALE:1.0507009873554805,ERF_P:.3275911,ERF_A1:.254829592,ERF_A2:-.284496736,ERF_A3:1.421413741,ERF_A4:-1.453152027,ERF_A5:1.061405429,warn:function(...e){C().getBool("IS_TEST")||console.warn(...e)},log:function(...e){C().getBool("IS_TEST")||console.log(...e)},mergeRealAndImagArrays:function(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);const n=new Float32Array(2*e.length);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n},splitRealAndImagArrays:function(e){const t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}},complexWithEvenIndex:function(e){const t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let 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r}}),Nl=Object.freeze({__proto__:null,nonMaxSuppressionV3Impl:xi,nonMaxSuppressionV4Impl:Ei,nonMaxSuppressionV5Impl:Si,whereImpl:Ka});e.Abs="Abs",e.Acos="Acos",e.Acosh="Acosh",e.AdadeltaOptimizer=pl,e.AdagradOptimizer=fl,e.AdamOptimizer=ml,e.AdamaxOptimizer=gl,e.Add="Add",e.AddN="AddN",e.All="All",e.Any="Any",e.ArgMax="ArgMax",e.ArgMin="ArgMin",e.Asin="Asin",e.Asinh="Asinh",e.Atan="Atan",e.Atan2="Atan2",e.Atanh="Atanh",e.AvgPool="AvgPool",e.AvgPool3D="AvgPool3D",e.AvgPool3DGrad="AvgPool3DGrad",e.AvgPoolGrad="AvgPoolGrad",e.BatchMatMul="BatchMatMul",e.BatchToSpaceND="BatchToSpaceND",e.Bincount="Bincount",e.BroadcastTo="BroadcastTo",e.Cast="Cast",e.Ceil="Ceil",e.ClipByValue="ClipByValue",e.Complex="Complex",e.ComplexAbs="ComplexAbs",e.Concat="Concat",e.Conv2D="Conv2D",e.Conv2DBackpropFilter="Conv2DBackpropFilter",e.Conv2DBackpropInput="Conv2DBackpropInput",e.Conv3D="Conv3D",e.Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",e.Conv3DBackpropInputV2="Conv3DBackpropInputV2",e.Cos="Cos",e.Cosh="Cosh",e.CropAndResize="CropAndResize",e.Cumsum="Cumsum",e.DataStorage=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new 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