@vladmandic/face-api/dist/face-api.js

JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS

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t==="string"?Hd(e):Er([e],t)}function Fk(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function Er(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=te(e)),oe().getBool("DEBUG")&&Cr(e,t),Fk(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){const n=new Uint8Array(e.length);for(let s=0;s<n.length;++s)Math.round(e[s])!==0&&(n[s]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Kn(){return oe().platform.now()}function lT(e,t){return oe().platform.fetch(e,t)}function Hd(e,t="utf-8"){return t=t||"utf-8",oe().platform.encode(e,t)}function rh(e,t="utf-8"){return t=t||"utf-8",oe().platform.decode(e,t)}var _k=Object.freeze({__proto__:null,createScalarValue:cT,toTypedArray:Er,now:Kn,fetch:lT,encodeString:Hd,decodeString:rh,shuffle:I,clamp:S,nearestLargerEven:T,sum:C,randUniform:D,distSquared:_,assert:A,assertShapesMatch:B,assertNonNull:ne,flatten:te,sizeFromShape:P,isScalarShape:ge,arraysEqual:ae,isInt:Le,tanh:ve,sizeToSquarishShape:Ve,createShuffledIndices:at,rightPad:pt,repeatedTry:$t,inferFromImplicitShape:Gt,parseAxisParam:qe,squeezeShape:ln,getTypedArrayFromDType:bt,getArrayFromDType:ws,checkConversionForErrors:Cr,isValidDtype:Rr,hasEncodingLoss:Ta,isTypedArray:hn,bytesPerElement:py,bytesFromStringArray:Rx,isString:qi,isBoolean:Ox,isNumber:ld,inferDtype:Aa,isFunction:Or,nearestDivisor:hd,computeStrides:je,toNestedArray:Ls,makeOnesTypedArray:my,makeZerosTypedArray:va,makeZerosNestedTypedArray:fy,assertNonNegativeIntegerDimensions:gy,locToIndex:Ws,indexToLoc:vo,isPromise:No});class Wk{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new Uk)}profileKernel(e,t,n){let s;const i=()=>{s=n()},o=this.backendTimer.time(i);for(let c=0;c<s.length;c++){const h=s[c];h.data().then(d=>{$k(d,h.dtype,e)})}const a={kernelName:e,outputs:s,inputs:t,timeMs:o.then(c=>c.kernelMs),extraInfo:o.then(c=>c.getExtraProfileInfo!=null?c.getExtraProfileInfo():"")};return a}logKernelProfile(e){const{kernelName:t,outputs:n,timeMs:s,inputs:i,extraInfo:o}=e;n.forEach(a=>{Promise.all([a.data(),s,o]).then(c=>{this.logger.logKernelProfile(t,a,c[0],c[1],i,c[2])})})}}function $k(e,t,n){if(t!=="float32")return!1;for(let s=0;s<e.length;s++){const i=e[s];if(isNaN(i)||!isFinite(i))return console.warn(`Found ${i} in the result of '${n}'`),!0}return!1}class Uk{logKernelProfile(e,t,n,s,i,o){const a=typeof s=="number"?pt(`${s}ms`,9):s.error,c=pt(e,25),h=t.rank,d=t.size,m=pt(t.shape.toString(),14);let f="";for(const b in i){const w=i[b];if(w!=null){const L=w.shape||t.shape,x=L.length;f+=`${b}: ${x}D ${x>0?L:""} `}}console.log(`%c${c}	%c${a}	%c${h}D 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Error(`Cannot compute gradient: gradient function not found for ${o.kernelName}.`);const c=o.gradient(a);for(const h in o.inputs){if(!(h in c))throw new Error(`Cannot backprop through input ${h}. 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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Ii().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>rh(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Ii().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Ii().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Wa.print(this,e)}clone(){return this.throwIfDisposed(),Wa.clone(this)}toString(e=!1){const t=this.dataSync();return Pk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Wa.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ii().makeVariable(this,e,t,n)}}Object.defineProperty(ee,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class lh extends ee{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ae(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ii().disposeTensor(this),this.dataId=e.dataId,Ii().incRef(this,null)}dispose(){Ii().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(lh,Symbol.hasInstance,{value:e=>e instanceof ee&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var cb;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(cb||(cb={}));var lb;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(lb||(lb={}));var hb;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(hb||(hb={}));var ub;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ub||(ub={}));const Hk={float32:hb,int32:cb,bool:lb,complex64:ub};function $n(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Hk[e][t]}function jd(e){return $n(e,"int32")}function Yt(e,t){if(e.dtype===t.dtype)return[e,t];const n=$n(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function pT(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Kd(e,t){return t.some(n=>n.id===e.id)}function ji(e){const t=[],n=new Set;return mT(e,t,n),t}function mT(e,t,n){if(e==null)return;if(e instanceof ee){t.push(e);return}if(!qk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),mT(o,t,n))}}function qk(e){return Array.isArray(e)||typeof e=="object"}var jk=Object.freeze({__proto__:null,makeTypesMatch:Yt,assertTypesMatch:pT,isTensorInList:Kd,getTensorsInContainer:ji});class fT{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class hh{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new fT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Wk(this.backendInstance),!0}setupRegisteredKernels(){const e=Gd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Gd(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof y)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return hh.nextTensorId++}nextVariableId(){return hh.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return G.runKernelFunc(d=>d.cast(o,a),c,null,Na,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,f=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=ib(s,this.backendName);let L;if(w!=null)b=()=>{const v=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,N);const O=N.map(({dataId:E,shape:k,dtype:F})=>this.makeTensorFromDataId(E,k,F));if(d){let E=this.getTensorsForGradient(s,t,O);if(E==null){a==null&&(a=[]);const k=O.filter((F,U)=>a[U]);E=(o||[]).slice().concat(k)}h=this.saveTensorsForBackwardMode(E)}return O};else{const v=N=>{if(!d)return;h=N.map(O=>this.keep(this.clone(O)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,v));const O=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,O),O}}let x;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(x=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(x),c=x.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-f,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:x.timeMs,extraInfo:x.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=rb(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&qi(e[0])&&(i=e.map(c=>Hd(c)));const o=s.write(i,t,n),a=new ee(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=Rx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new ee(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new lh(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*py(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof lh||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=rb(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const f=n[m],b=va(f.size,f.dtype);return this.makeTensor(b,f.shape,f.dtype)}return d}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=ji(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(i instanceof ee,()=>"The result y returned by f() must be a tensor.");const o=Bk(this.state.activeTape,t,i);if(!s&&o.length===0&&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 a={};a[i.id]=n==null?Kk(i.shape):n,Mk(a,o,h=>this.tidy(h),Xk);const c=t.map(h=>a[h.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(h=>{for(const d of h.saved)d.dispose()}),this.state.activeTape=null),{value:i,grads:c}})}customGrad(e){return A(Or(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(i=>i instanceof ee),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n;const s={};return t.forEach((i,o)=>{s[o]=i}),this.runKernelFunc((i,o)=>(n=e(...t,o),A(n.value instanceof ee,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(Or(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s,(i,o)=>{const a=n.gradFunc(i,o),c=Array.isArray(a)?a:[a];A(c.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(...)."),A(c.every(d=>d instanceof ee),()=>"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 h={};return c.forEach((d,m)=>{h[m]=()=>d}),h})}}readSync(e){const t=this.state.tensorInfo.get(e);return t.backend.readSync(e)}read(e){const t=this.state.tensorInfo.get(e);return t.backend.read(e)}async time(e){const t=Kn(),n=await this.backend.time(e);return n.wallMs=Kn()-t,n}track(e){return this.state.activeScope!=null&&(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 fT;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}}hh.nextTensorId=0,hh.nextVariableId=0;function Kk(e){const t=my(P(e),"float32");return G.makeTensor(t,e,"float32")}function gT(){const e=Fx();if(e._tfengine==null){const t=new kx(e);e._tfengine=new hh(t)}return Ck(e._tfengine.ENV),Vk(()=>e._tfengine),e._tfengine}const G=gT();function Xk(e,t){const n={a:e,b:t};return G.runKernelFunc((s,i)=>{const o=s.add(e,t);return i([e,t]),o},n,null,Co)}function Jk(){return typeof navigator!="undefined"&&navigator!=null}function yT(){if(Jk()){const e=navigator.userAgent||navigator.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( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function db(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Zk=Object.freeze({__proto__:null,isMobile:yT,isBrowser:db});const Ki=oe();Ki.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.")}),Ki.registerFlag("IS_BROWSER",()=>db()),Ki.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),Ki.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),Ki.registerFlag("PROD",()=>!1),Ki.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ki.getBool("DEBUG")),Ki.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),Ki.registerFlag("IS_TEST",()=>!1);function xi(e,t){let n=e;if(hn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||hn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&oe().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&bT(e,s,[]),s}function bT(e,t,n){if(n=n||[],!Array.isArray(e)&&!hn(e)){A(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}A(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),A(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)bT(e[i],s,n.concat(i))}function wT(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof ee)return wT(s,e.dtype,t,n),e;let i=Aa(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),wT(s,i,t,n),e==null||!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.construct