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face-api
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face-api/dist/face-api.node.js

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tk=Object.freeze({__proto__:null,shuffle:Ly,clamp:Hl,nearestLargerEven:Sy,sum:iT,randUniform:KD,distSquared:XD,assert:k,assertShapesMatch:dt,assertNonNull:wo,flatten:Ji,sizeFromShape:we,isScalarShape:JD,arraysEqual:ot,isInt:Ut,tanh:ZD,sizeToSquarishShape:Sd,createShuffledIndices:QD,rightPad:Lo,repeatedTry:Iy,inferFromImplicitShape:Id,parseAxisParam:ft,squeezeShape:Rr,getTypedArrayFromDType:wn,getArrayFromDType:So,checkConversionForErrors:rT,isValidDtype:oT,hasEncodingLoss:xy,isTypedArray:Ln,bytesPerElement:Ty,bytesFromStringArray:aT,isString:Or,isBoolean:cT,isNumber:xd,inferDtype:Oa,isFunction:Er,nearestDivisor:Td,computeStrides:Ot,createScalarValue:lT,toTypedArray:Dr,toNestedArray:Ls,makeOnesTypedArray:Ay,makeZerosTypedArray:Ea,makeZerosNestedTypedArray:vy,now:qn,assertNonNegativeIntegerDimensions:Ny,fetch:uT,encodeString:Cy,decodeString:Yl,locToIndex:ti,indexToLoc:Da});class nk{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new ik)}profileKernel(e,t,n){let s;const i=()=>{s=n()},o=this.backendTimer.time(i);for(let c=0;c<s.length;c++){const u=s[c];u.data().then(p=>{sk(p,u.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 sk(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 ik{logKernelProfile(e,t,n,s,i,o){const a=typeof s=="number"?Lo(`${s}ms`,9):s.error,c=Lo(e,25),u=t.rank,p=t.size,m=Lo(t.shape.toString(),14);let y="";for(const b in i){const w=i[b];if(w!=null){const I=w.shape||t.shape,T=I.length;y+=`${b}: ${T}D ${T>0?I:""} `}}console.log(`%c${c} %c${a} %c${u}D ${m} %c${p} %c${y} %c${o}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}}function rk(e,t,n){const s={},i={};for(let u=0;u<t.length;u++)s[t[u].id]=!0;for(let u=0;u<e.length;u++){const p=e[u],m=p.inputs;for(const y in m){const b=m[y];let w=!1;for(let I=0;I<t.length;I++)if(s[b.id]){p.outputs.forEach(T=>s[T.id]=!0),w=!0,i[p.id]=!0;break}if(w)break}}const o={};o[n.id]=!0;const a={};for(let u=e.length-1;u>=0;u--){const p=e[u],m=p.inputs;for(let y=0;y<p.outputs.length;y++)if(o[p.outputs[y].id]){for(const b in m)o[m[b].id]=!0,a[p.id]=!0;break}}const c=[];for(let u=0;u<e.length;u++){const p=e[u];if(i[p.id]&&a[p.id]){const m={};for(const b in p.inputs){const w=p.inputs[b];s[w.id]&&(m[b]=w)}const y=Object.assign({},p);y.inputs=m,y.outputs=p.outputs,c.push(y)}}return c}function ok(e,t,n,s){for(let i=t.length-1;i>=0;i--){const o=t[i],a=[];if(o.outputs.forEach(u=>{const p=e[u.id];p!=null?a.push(p):a.push(null)}),o.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${o.kernelName}.`);const c=o.gradient(a);for(const u in o.inputs){if(!(u in c))throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(c)}.`);const p=n(()=>c[u]());if(p.dtype!=="float32")throw new Error(`Error in gradient for op ${o.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${p.dtype}'`);const m=o.inputs[u];if(!ot(p.shape,m.shape))throw new Error(`Error in gradient for op ${o.kernelName}. The gradient of input '${u}' has shape '${p.shape}', which does not match the shape of the input '${m.shape}'`);if(e[m.id]==null)e[m.id]=p;else{const y=e[m.id];e[m.id]=s(y,p),y.dispose()}}}}const dT=20,ql=3,Ry=7;function ak(e,t,n,s){const i=Ot(t),o=ck(e,t,n,i),a=t.length,c=Ad(e,t,n,i,o),u=["Tensor"];return s&&(u.push(` dtype: ${n}`),u.push(` rank: ${a}`),u.push(` shape: [${t}]`),u.push(" values:")),u.push(c.map(p=>" "+p).join(`
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`;for(let T=2;T<u;T++)I+=`
`;return b[b.length-1]=" "+b[b.length-1]+"]"+(o?"":I),b}function Kl(e){const t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}class kr{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=we(e),n!=null){const s=n.length;k(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||So(t,this.size),this.strides=Ot(e)}set(e,...t){t.length===0&&(t=[0]),k(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);const n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(const s of e){if(s<0||s>=this.shape[t]){const i=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return vi().makeTensor(this.values,this.shape,this.dtype)}}let vi=null,ka=null,mT=null;function lk(e){vi=e}function hk(e){ka=e}function uk(e){mT=e}class Q{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=we(e),this.strides=Ot(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return ka.buffer(this.shape,this.dtype,e)}bufferSync(){return ka.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Ls(this.shape,e)}arraySync(){return Ls(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=vi().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>Yl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=vi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Yl(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 vi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;vi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ka.print(this,e)}clone(){return this.throwIfDisposed(),ka.clone(this)}toString(e=!1){const t=this.dataSync();return ak(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ka.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),vi().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Xl extends Q{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(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);vi().disposeTensor(this),this.dataId=e.dataId,vi().incRef(this,null)}dispose(){vi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Xl,Symbol.hasInstance,{value:e=>e instanceof Q&&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 Oy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Oy||(Oy={}));var Ey;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Ey||(Ey={}));var Dy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Dy||(Dy={}));var ky;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ky||(ky={}));const dk={float32:Dy,int32:Oy,bool:Ey,complex64:ky};function Cn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return dk[e][t]}function vd(e){return Cn(e,"int32")}function Bt(e,t){if(e.dtype===t.dtype)return[e,t];const n=Cn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function fT(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Nd(e,t){return t.some(n=>n.id===e.id)}function Zi(e){const t=[],n=new Set;return gT(e,t,n),t}function gT(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!pk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),gT(o,t,n))}}function pk(e){return Array.isArray(e)||typeof e=="object"}var mk=Object.freeze({__proto__:null,makeTypesMatch:Bt,assertTypesMatch:fT,isTensorInList:Nd,getTensorsInContainer:Zi});class yT{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class Jl{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new yT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 nk(this.backendInstance),!0}setupRegisteredKernels(){const e=wd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=wd(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 f)&&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 Jl.nextTensorId++}nextVariableId(){return Jl.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},u={dtype:a};return V.runKernelFunc(p=>p.cast(o,a),c,null,ul,u)}}),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,u=[];const p=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=yy(s,this.backendName);let I;if(w!=null)b=()=>{const v=this.backend.numDataIds();I=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(I)?I:[I];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(p){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}u=this.saveTensorsForBackwardMode(D)}return E};else{const v=N=>{if(!p)return;u=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();I=this.tidy(()=>e(this.backend,v));const E=Array.isArray(I)?I:[I];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),p&&this.addTapeNode(s,t,c,n,u,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(I)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=by(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(u=>t[u])):a=i.map(u=>t[u]);const c=n.filter((u,p)=>o[p]);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"&&Or(e[0])&&(i=e.map(c=>Cy(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),u=aT(i);this.state.numBytes+=u-c.bytes,c.bytes=u}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(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 Xl(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*Ty(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 Xl||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=by(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=u=>(u=u.map((p,m)=>{if(p==null){const y=n[m],b=Ea(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return p}),s(u.length>1?u:u[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=Zi(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(k(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));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=rk(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?fk(i.shape):n,ok(a,o,u=>this.tidy(u),gk);const c=t.map(u=>a[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(const p of u.saved)p.dispose()}),this.state.activeTape=null),{value:i,grads:c}})}customGrad(e){return k(Er(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{k(t.every(i=>i instanceof Q),()=>"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),k(n.value instanceof Q,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),k(Er(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];k(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(...)."),k(c.every(p=>p instanceof Q),()=>"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 u={};return c.forEach((p,m)=>{u[m]=()=>p}),u})}}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=qn(),n=await this.backend.time(e);return n.wallMs=qn()-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 yT;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}}Jl.nextTensorId=0,Jl.nextVariableId=0;function fk(e){const t=Ay(we(e),"float32");return V.makeTensor(t,e,"float32")}function bT(){const e=ne();if(e._tfengine==null){const t=new L(e);e._tfengine=new Jl(t)}return $(e._tfengine.ENV),lk(()=>e._tfengine),e._tfengine}const V=bT();function gk(e,t){const n={a:e,b:t};return V.runKernelFunc((s,i)=>{const o=s.add(e,t);return i([e,t]),o},n,null,xe)}function yk(){return typeof navigator!="undefined"&&navigator!=null}function wT(){if(yk()){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 Fy(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var bk=Object.freeze({__proto__:null,isMobile:wT,isBrowser:Fy});const Qi=C();Qi.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.")}),Qi.registerFlag("IS_BROWSER",()=>Fy()),Qi.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),Qi.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),Qi.registerFlag("PROD",()=>!1),Qi.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Qi.getBool("DEBUG")),Qi.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),Qi.registerFlag("IS_TEST",()=>!1);function Ni(e,t){let n=e;if(Ln(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||Ln(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&C().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&LT(e,s,[]),s}function LT(e,t,n){if(n=n||[],!Array.isArray(e)&&!Ln(e)){k(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}k(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),k(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)LT(e[i],s,n.concat(i))}function ST(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof Q)return ST(s,e.dtype,t,n),e;let i=Oa(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),ST(s,i,t,n),e==null||!Ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const u=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`)}const o=Ni(e,i);!Ln(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Dr(e,i):Ji(e,[],a);return V.makeTensor(c,o,i)}function Zl(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);const i=e;return i.map((o,a)=>W(o,`${t}[${a}]`,n),s)}const IT="__op";function P(e){const t=Object.keys(e);if(t.length!==1)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 s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+IT;const i=(...o)=>{V.startScope(n);try{const a=s(...o);return a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),V.endScope(a),a}catch(a){throw V.endScope(null),a}};return Object.defineProperty(i,"name",{value:n,configurable:!0}),i}function wk(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");dt(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return V.runKernelFunc(i,o,null,Rg)}const Ci=P({complex_:wk});function Fr(e,t,n,s){if(s==null&&(s=Oa(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!Ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Ny(t);const i=we(t),o=we(n);k(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],u=a===n.length-1?c!==we(t.slice(a)):!0;k(n[a]===t[a]||!u,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!Ln(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Dr(e,s):Ji(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=Ni(e,n);return Fr(e,t,s,n)}const _y={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const Cd=4;async function Wy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],u=Array.isArray(e)?e[a].tensor:e[c];if(u.dtype!=="float32"&&u.dtype!=="int32"&&u.dtype!=="bool"&&u.dtype!=="string"&&u.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${u.dtype}`);const p={name:c,shape:u.shape,dtype:u.dtype};if(u.dtype==="string"){const m=new Promise(async y=>{const b=await u.bytes(),w=b.reduce((v,N)=>v+N.length,0)+Cd*b.length,I=new Uint8Array(w);let T=0;for(let v=0;v<b.length;v++){const N=b[v],E=new Uint8Array(new Uint32Array([N.length]).buffer);I.set(E,T),T+=Cd,I.set(N,T),T+=N.length}y(I)});s.push(m)}else s.push(u.data());t!=null&&(p.group=t),n.push(p)}const o=await Promise.all(s);return{data:Lk(o),specs:n}}function Rd(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,u=o.shape,p=we(u);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=_y[y.dtype],w=e.slice(i,i+p*b),I=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(I.length);for(let T=0;T<I.length;T++){const v=I[T];m[T]=v*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=vk()),m=s(I);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(I.length);for(let T=0;T<I.length;T++){const v=I[T];m[T]=Math.round(v*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+Cd))[0];i+=Cd;const I=new Uint8Array(e.slice(i,i+w));m.push(I),i+=w}}else{const y=_y[c],b=e.slice(i,i+p*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),I=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],I[N]=m[N*2+1];const T=en(w,u,"float32"),v=en(I,u,"float32");n[a]=Ci(T,v)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*y}c!=="complex64"&&(n[a]=en(m,u,c))}return n}function Lk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const $y=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function xT(e){return $y?Buffer.byteLength(e):new Blob([e]).size}function Sk(e){if($y)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function Ik(e){if($y){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function Od(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function TT(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function Ql(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:xT(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:xT(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function xk(){const e=n=>{let s=n<<13,i=0;for(;(s&8388608)===0;)i-=8388608,s<<=1;return s&=~8388608,i+=947912704,s|i},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function Tk(){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}function Ak(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function vk(){const e=xk(),t=Tk(),n=Ak();return s=>{const i=new ArrayBuffer(4*s.length),o=new Uint32Array(i);for(let a=0;a<s.length;a++){const c=s[a],u=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=u}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const Nk=e=>Xt.registerSaveRouter(e),Ck=e=>Xt.registerLoadRouter(e),Uy=e=>Xt.getSaveHandlers(e),By=(e,t)=>Xt.getLoadHandlers(e,t);const Ed="tensorflowjs",My=1,Io="models_store",_r="model_info_store";async function NQ(){const e=Py();return new Promise((t,n)=>{const s=e.deleteDatabase(Ed);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Py(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function zy(e){const t=e.result;t.createObjectStore(Io,{keyPath:"modelPath"}),t.createObjectStore(_r,{keyPath:"modelPath"})}class xo{constructor(e){if(this.indexedDB=Py(),e==null||!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((n,s)=>{const i=this.indexedDB.open(Ed,My);i.onupgradeneeded=()=>zy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Io,"readonly"),c=a.objectStore(Io),u=c.get(this.modelPath);u.onsuccess=()=>{if(u.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(u.result.modelArtifacts)},u.onerror=p=>(o.close(),s(u.error)),a.oncomplete=()=>o.close()}else{const a=Ql(t),c=o.transaction(_r,"readwrite");let u=c.objectStore(_r);const p=u.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;p.onsuccess=()=>{m=o.transaction(Io,"readwrite");const y=m.objectStore(Io),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{u=c.objectStore(_r);const I=u.delete(this.modelPath);I.onsuccess=()=>(o.close(),s(b.error)),I.onerror=T=>(o.close(),s(b.error))}},p.onerror=y=>(o.close(),s(p.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}xo.URL_SCHEME="indexeddb://";const AT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME))?Rk(e.slice(xo.URL_SCHEME.length)):null;Xt.registerSaveRouter(AT),Xt.registerLoadRouter(AT);function Rk(e){return new xo(e)}function Ok(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}class Ek{constructor(){this.indexedDB=Py()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(Ed,My);n.onupgradeneeded=()=>zy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(_r,"readonly"),o=i.objectStore(_r),a=o.getAll();a.onsuccess=()=>{const c={};for(const u of a.result)c[u.modelPath]=u.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=Ok(e),new Promise((t,n)=>{const s=this.indexedDB.open(Ed,My);s.onupgradeneeded=()=>zy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(_r,"readwrite"),a=o.objectStore(_r),c=a.get(e);let u;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const p=a.delete(e),m=()=>{u=i.transaction(Io,"readwrite");const y=u.objectStore(Io),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};p.onsuccess=m,p.onerror=y=>(m(),i.close(),n(c.error))}},c.onerror=p=>(i.close(),n(c.error)),o.oncomplete=()=>{u==null?i.close():u.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ri="/",To="tensorflowjs_models",vT="info",Dk="model_topology",kk="weight_specs",Fk="weight_data",_k="model_metadata";function CQ(){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=To+Ri;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=CT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function NT(e){return{info:[To,e,vT].join(Ri),topology:[To,e,Dk].join(Ri),weightSpecs:[To,e,kk].join(Ri),weightData:[To,e,Fk].join(Ri),modelMetadata:[To,e,_k].join(Ri)}}function CT(e){const t=e.split(Ri);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ri)}function Wk(e){return e.startsWith(Ao.URL_SCHEME)?e.slice(Ao.URL_SCHEME.length):e}class Ao{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=NT(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Ql(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,Sk(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")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(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=Ik(o),t}}Ao.URL_SCHEME="localstorage://";const RT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Ao.URL_SCHEME))?$k(e.slice(Ao.URL_SCHEME.length)):null;Xt.registerSaveRouter(RT),Xt.registerLoadRouter(RT);function $k(e){return new Ao(e)}class Uk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=To+Ri,n=Ri+vT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=CT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=Wk(e);const t=NT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Fa="://";class Ss{constructor(){this.managers={}}static getInstance(){return Ss.instance==null&&(Ss.instance=new Ss),Ss.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Fa)&&(e=e.slice(0,e.indexOf(Fa))),k(e.length>0,()=>"scheme must not be an empty string.");const n=Ss.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function Dd(e){if(e.indexOf(Fa)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Ss.getSchemes().join(",")}`);return{scheme:e.split(Fa)[0],path:e.split(Fa)[1]}}async function OT(e,t,n=!1){k(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=Xt.getLoadHandlers(e);k(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),k(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=Xt.getSaveHandlers(t);k(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),k(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=Dd(e).scheme,u=Dd(e).path,p=c===Dd(e).scheme,m=await i.load();n&&p&&await Ss.getManager(c).removeModel(u);const y=await a.save(m);return n&&!p&&await Ss.getManager(c).removeModel(u),y.modelArtifactsInfo}async function Bk(){const e=Ss.getSchemes(),t={};for(const n of e){const s=await Ss.getManager(n).listModels();for(const i in s){const o=n+Fa+i;t[o]=s[i]}}return t}async function Mk(e){const t=Dd(e),n=Ss.getManager(t.scheme);return n.removeModel(t.path)}async function Pk(e,t){const n=!1;return OT(e,t,n)}async function zk(e,t){const n=!0;return OT(e,t,n)}class Gk{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(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 Gk);try{Ss.registerManager(Ao.URL_SCHEME,new Uk)}catch(e){}try{Ss.registerManager(xo.URL_SCHEME,new Ek)}catch(e){}}const Vk={importFetch:()=>i2()};let _a;function RQ(){_a=null}function OQ(e){_a=e}function EQ(){return _a}class Hk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(_a==null&&(_a=Vk.importFetch()),_a(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new Hk);function Qe(e,t="float32",n){return t=t||"float32",Ny(e),new kr(e,t,n)}function Yk(e,t){const n=W(e,"x","cast");if(!oT(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,ul,i)}const ve=P({cast_:Yk});function qk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,Sl)}const Wr=P({clone_:qk});function ET(e,t=!1){console.log(e.toString(t))}bT();const jk={buffer:Qe,cast:ve,clone:Wr,print:ET};hk(jk);const Kk="model",Xk=".json",Jk=".weights.bin";function DT(e){return new Promise(t=>setTimeout(t)).then(e)}class Wa{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Wa.URL_SCHEME)&&(e=e.slice(Wa.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Kk),this.modelTopologyFileName=e+Xk,this.weightDataFileName=e+Jk}async save(e){if(typeof document=="undefined")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}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await DT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await DT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ql(e)}}}}Wa.URL_SCHEME="downloads://";class Zk{constructor(e){if(e==null||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,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const u=a.weightsManifest;if(u==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let p;try{p=this.checkManifestAndWeightFiles(u,t)}catch(w){s(w);return}const m=[],y=[],b=[];u.forEach(w=>{w.paths.forEach(I=>{y.push(I),b.push(null)}),m.push(...w.weights)}),u.forEach(w=>{w.paths.forEach(I=>{const T=new FileReader;T.onload=v=>{const N=v.target.result,E=y.indexOf(I);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:Od(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=v=>s(`Failed to weights data from file of path '${I}'.`),T.readAsArrayBuffer(p[I])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. 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Please use float32 or int32 tensors.`);const a=await n.data(),c=n.dtype==="float32"?255:1,u=new Uint8ClampedArray(i*s*4);for(let p=0;p<s*i;++p){const m=[0,0,0,255];for(let b=0;b<o;b++){const w=a[p*o+b];if(n.dtype==="float32"){if(w<0||w>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${w}.`)}else if(n.dtype==="int32"&&(w<0||w>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${w}.`);o===1?(m[0]=w*c,m[1]=w*c,m[2]=w*c):m[b]=w*c}const y=p*4;u[y+0]=Math.round(m[0]),u[y+1]=Math.round(m[1]),u[y+2]=Math.round(m[2]),u[y+3]=Math.round(m[3])}if(t!=null){t.width=i,t.height=s;const p=t.getContext("2d"),m=new ImageData(u,i,s);p.putImageData(m,0,0)}return n!==e&&n.dispose(),u}const BT=P({fromPixels_:gF});var bF=Object.freeze({__proto__:null,toPixels:yF,fromPixels:BT});function Fd(e,t){if(e.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[t.rank-1]>e.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[t.rank-1]} vs. ${e.rank}`);if(e.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);const n=t.shape,s=n[n.length-1];let i=1;for(let p=0;p<n.length-1;++p)i*=n[p];const o=e.shape,a=n.slice();a.pop();let c=1;for(let p=s;p<e.rank;++p)c*=o[p],a.push(o[p]);const u=[...Ot(e.shape).map(p=>p/c),1].slice(0,s);return[a,i,c,u]}var wF=Object.freeze({__proto__:null,prepareAndValidate:Fd});function qy(e,t,n){const s=t.rank>1?t.shape[t.rank-1]:1,i=t.rank>1?t.rank-1:1,o=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${i}.`;if(n.rank<i)throw new Error(o+` update.rank < ${i}. `);if(e.length<s+(n.rank-i))throw new Error(o+` Output shape length < ${s+(n.rank-i)}`);if(n.rank!==i+e.length-s)throw new Error(o+` update.rank != ${i+e.length-s}`);for(let a=0;a<i;++a)if(n.shape[a]!==t.shape[a])throw new Error(o+` updates.shape[${a}] (${n.shape[a]}) != indices.shape[${a}] (${t.shape[a]}).`);for(let 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Ja(e,t,n,s,i,o){s==null&&(s=.5),i==null&&(i=Number.NEGATIVE_INFINITY),o==null&&(o=0);const a=e.shape[0];return n=Math.min(n,a),k(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${s}'`),k(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),k(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),k(t.rank===1,()=>"scores must be a 1D tensor"),k(t.shape[0]===a,()=>`scores has incompatible shape with boxes. Expected ${a}, but was ${t.shape[0]}`),k(0<=o&&o<=1,()=>`softNmsSigma must be in [0, 1], but was '${o}'`),{maxOutputSize:n,iouThreshold:s,scoreThreshold:i,softNmsSigma:o}}function bB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY){const o=W(e,"boxes","nonMaxSuppression"),a=W(t,"scores","nonMaxSuppression"),c=Ja(o,a,n,s,i);n=c.maxOutputSize,s=c.iouThreshold,i=c.scoreThreshold;const u={maxOutputSize:n,iouThreshold:s,scoreThreshold:i};return V.runKernelFunc(p=>p.nonMaxSuppression(o,a,n,s,i),{boxes:o,scores:a},null,Kg,u)}const wB=P({nonMaxSuppression_:bB});function LB(e,t,n){const s=SB(e,t,n),i=s<0?-(s+1):s;e.splice(i,0,t)}function SB(e,t,n){return xB(e,t,n||IB)}function IB(e,t){return e>t?1:e<t?-1:0}function xB(e,t,n){let s=0,i=e.length,o=0,a=!1;for(;s<i;){o=s+(i-s>>>1);const c=n(t,e[o]);c>0?s=o+1:(i=o,a=!c)}return a?s:-s-1}function wp(e,t,n,s,i){return Yb(e,t,n,s,i,0).selectedIndices}function Lp(e,t,n,s,i,o){return Yb(e,t,n,s,i,0,!1,o,!0)}function Sp(e,t,n,s,i,o){return Yb(e,t,n,s,i,o,!0)}function Yb(e,t,n,s,i,o,a=!1,c=!1,u=!1){const p=[];for(let v=0;v<t.length;v++)t[v]>i&&p.push({score:t[v],boxIndex:v,suppressBeginIndex:0});p.sort(KA);const m=o>0?-.5/o:0,y=[],b=[];for(;y.length<n&&p.length>0;){const v=p.pop(),{score:N,boxIndex:E,suppressBeginIndex:D}=v;if(N<i)break;let F=!1;for(let _=y.length-1;_>=D;--_){const B=TB(e,E,y[_]);if(B>=s){F=!0;break}if(v.score=v.score*AB(s,m,B),v.score<=i)break}v.suppressBeginIndex=y.length,F||(v.score===N?(y.push(E),b.push(v.score)):v.score>i&&LB(p,v,KA))}const w=y.length,I=n-w;c&&I>0&&(y.push(...new Array(I).fill(0)),b.push(...new Array(I).fill(0)));const T={selectedIndices:rs(y,"int32")};return a&&(T.selectedScores=rs(b,"float32")),u&&(T.validOutputs=Ne(w,"int32")),T}function TB(e,t,n){const s=e.subarray(t*4,t*4+4),i=e.subarray(n*4,n*4+4),o=Math.min(s[0],s[2]),a=Math.min(s[1],s[3]),c=Math.max(s[0],s[2]),u=Math.max(s[1],s[3]),p=Math.min(i[0],i[2]),m=Math.min(i[1],i[3]),y=Math.max(i[0],i[2]),b=Math.max(i[1],i[3]),w=(c-o)*(u-a),I=(y-p)*(b-m);if(w<=0||I<=0)return 0;const T=Math.max(o,p),v=Math.max(a,m),N=Math.min(c,y),E=Math.min(u,b),D=Math.max(N-T,0)*Math.max(E-v,0);return D/(w+I-D)}function AB(e,t,n){const s=Math.exp(t*n*n);return n<=e?s:0}function KA(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function vB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY){const o=W(e,"boxes","nonMaxSuppressionAsync"),a=W(t,"scores","nonMaxSuppressionAsync"),c=Ja(o,a,n,s,i);n=c.maxOutputSize,s=c.iouThreshold,i=c.scoreThreshold;const u=await Promise.all([o.data(),a.data()]),p=u[0],m=u[1],y=wp(p,m,n,s,i);return o!==e&&o.dispose(),a!==t&&a.dispose(),y}const NB=vB;function CB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=0){const a=W(e,"boxes","nonMaxSuppression"),c=W(t,"scores","nonMaxSuppression"),u=Ja(a,c,n,s,i,o);n=u.maxOutputSize,s=u.iouThreshold,i=u.scoreThreshold,o=u.softNmsSigma;const p={boxes:a,scores:c},m={maxOutputSize:n,iouThreshold:s,scoreThreshold:i,softNmsSigma:o},y=V.runKernel(ud,p,m);return{selectedIndices:y[0],selectedScores:y[1]}}const RB=P({nonMaxSuppressionWithScore_:CB});async function OB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=0){const a=W(e,"boxes","nonMaxSuppressionAsync"),c=W(t,"scores","nonMaxSuppressionAsync"),u=Ja(a,c,n,s,i,o);n=u.maxOutputSize,s=u.iouThreshold,i=u.scoreThreshold,o=u.softNmsSigma;const p=await Promise.all([a.data(),c.data()]),m=p[0],y=p[1],b=Sp(m,y,n,s,i,o);return a!==e&&a.dispose(),c!==t&&c.dispose(),b}const EB=OB;function DB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=!1){const a=W(e,"boxes","nonMaxSuppression"),c=W(t,"scores","nonMaxSuppression"),u=Ja(a,c,n,s,i,null),p=u.maxOutputSize,m=u.iouThreshold,y=u.scoreThreshold,b={boxes:a,scores:c},w={maxOutputSize:p,iouThreshold:m,scoreThreshold:y,padToMaxOutputSize:o},I=V.runKernel(hd,b,w);return{selectedIndices:I[0],validOutputs:I[1]}}const kB=P({nonMaxSuppressionPadded_:DB});async function FB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=!1){const a=W(e,"boxes","nonMaxSuppressionAsync"),c=W(t,"scores","nonMaxSuppressionAsync"),u=Ja(a,c,n,s,i,null),p=u.maxOutputSize,m=u.iouThreshold,y=u.scoreThreshold,[b,w]=await Promise.all([a.data(),c.data()]),I=Lp(b,w,p,m,y,o);return a!==e&&a.dispose(),c!==t&&c.dispose(),I}const _B=FB;function WB(e,t,n=!1){const s=W(e,"images","resizeBilinear");k(s.rank===3||s.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`),k(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,u=(b,w)=>(w([i]),b.resizeBilinear(i,a,c,n)),p={images:i},m={alignCorners:n,size:t},y=V.runKernelFunc(u,p,null,sy,m);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const XA=P({resizeBilinear_:WB});function $B(e,t,n=!1){const s=W(e,"images","resizeNearestNeighbor");k(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),k(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),k(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,u={images:i},p={alignCorners:n,size:t},m=(b,w)=>(w([i]),b.resizeNearestNeighbor(i,a,c,n)),y=V.runKernelFunc(m,u,null,ny,p);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const JA=P({resizeNearestNeighbor_:$B});function UB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(yh(0,o,1,"int32"),[-1,1]),u=yh(0,a,1,"int32"),p=Ce(c,u),m=Bs(Mr(p,Ne(+t,"int32")),tr(p,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(as(_i(K(s,[-1,o,a])).map(b=>$n(m,b,y))),i)}const BB=P({bandPart_:UB});function MB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in 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n=e.shape[0],s=e.shape[1];let i=Kd(n),o=Wr(e);const a=Gr([[1]],[1,1]);let c=Wr(a);const u=n>=s?s:n;for(let p=0;p<u;++p){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=st(o,[p,p],[n-p,1]),I=pp(w),T=st(o,[p,p],[1,1]),v=$n(Ts(T,0),Gr([[-1]]),Gr([[1]])),N=Ce(T,X(v,I)),E=_e(w,N);E.shape[0]===1?c=Wr(a):c=Mt([a,st(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=Pt(_e(at(v,N),I)),F=st(o,[p,0],[n-p,s]),_=X(D,c),B=Pe(c);if(p===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Mt([st(o,[0,0],[p,s]),q],0)}const U=Pe(_),Y=st(i,[0,p],[n,i.shape[1]-p]);if(p===0)i=Ce(Y,at(at(Y,c),U));else{const q=Ce(Y,at(at(Y,c),U));i=Mt([st(i,[0,0],[n,p]),q],1)}return[c,o,i]}),qe([m,y,b])}return!t&&n>s&&(i=st(i,[0,0],[n,s]),o=st(o,[0,0],[s,s])),[i,o]})}const GB=P({qr_:zB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function VB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return zt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,si(s.shape)),c=ve(Ue(Pr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const nr=P({computeWeightedLoss_:VB});function HB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),dt(i.shape,o.shape,"Error in absoluteDifference: ");const c=rn(Ce(i,o));return nr(c,a,s)}const YB=P({absoluteDifference_:HB});function qB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),dt(o.shape,a.shape,"Error in cosineDistance: ");const u=Ne(1),p=Ce(u,Ue(X(o,a),n,!0));return nr(p,c,i)}const jB=P({cosineDistance_:qB});function KB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),dt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const u=Fi(Ce(c,X(i,o)));return nr(u,a,s)}const XB=P({hingeLoss_:KB});function JB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),dt(o.shape,a.shape,"Error in huberLoss: ");const u=Ne(s),p=rn(Ce(a,o)),m=ko(p,u),y=Ce(p,m),b=be(X(Ne(.5),Lt(m)),X(u,y));return nr(b,c,i)}const ZB=P({huberLoss_:JB});function QB(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),dt(o.shape,a.shape,"Error in logLoss: ");const u=Ne(1),p=Ne(s),m=Pt(X(o,is(be(a,p)))),y=X(Ce(u,o),is(be(Ce(u,a),p))),b=Ce(m,y);return nr(b,c,i)}const eM=P({logLoss_:QB});function tM(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),dt(i.shape,o.shape,"Error in meanSquaredError: ");const c=Sh(i,o);return nr(c,a,s)}const nM=P({meanSquaredError_:tM});function sM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");dt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Fi(s),o=X(s,n),a=Jd(xs(Pt(rn(s))));return be(Ce(i,o),a)}function iM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),dt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const p=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,p)),X(y,p))}const u=sM(o,a);return nr(u,c,i)}const rM=P({sigmoidCrossEntropy_:iM});function oM(e,t,n=-1){if(n===-1&&(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}`);const s=Di((i,o,a)=>{const c=!0,u=vb(o,[n],c),p=Ce(ve(o,"float32"),u);a([i,p]);const m=Pt(X(p,i)),y=Ue(m,[n]),b=(w,I)=>{const[T,v]=I,N=En(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),xs(v))),X(K(w,N),Ce(xs(v),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function aM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),dt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const p=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,p)),_e(p,y))}const u=oM(o,a);return nr(u,c,i)}const cM=P({softmaxCrossEntropy_:aM});const lM={fft:wh,ifft:ja,rfft:Lh,irfft:hp},hM={hammingWindow:aB,hannWindow:qA,frame:jA,stft:uB},Vr={flipLeftRight:fB,resizeNearestNeighbor:JA,resizeBilinear:XA,rotateWithOffset:yB,cropAndResize:pB,nonMaxSuppression:wB,nonMaxSuppressionAsync:NB,nonMaxSuppressionWithScore:RB,nonMaxSuppressionWithScoreAsync:EB,nonMaxSuppressionPadded:kB,nonMaxSuppressionPaddedAsync:_B},QA={bandPart:BB,gramSchmidt:PB,qr:GB},uM={absoluteDifference:YB,computeWeightedLoss:nr,cosineDistance:jB,hingeLoss:XB,huberLoss:ZB,logLoss:eM,meanSquaredError:nM,sigmoidCrossEntropy:rM,softmaxCrossEntropy:cM};class sr extends No{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return qe(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ab(e,t)}dispose(){this.iterations_!=null&&qe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(sr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class xh extends sr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>et(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;ee(()=>{const p=be(X(c,this.rho),X(Lt(a),1-this.rho)),m=X(_e(Sn(be(u,this.epsilon)),Sn(be(c,this.epsilon))),a),y=be(X(u,this.rho),X(Lt(m),1-this.rho));c.assign(p),u.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(qe(this.accumulatedGrads.map(e=>e.variable)),qe(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}xh.className="Adadelta",ge(xh);class Th extends sr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>hh(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,Lt(o));a.assign(c);const u=be(X(_e(o,Sn(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&qe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Th.className="Adagrad",ge(Th);class Ah extends sr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>et(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>et(a).variable(c))});const u=Array.isArray(e)?e[o].tensor:e[i];if(u==null)return;const p=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(p,this.beta1),X(u,1-this.beta1)),b=be(X(m,this.beta2),X(Lt(u),1-this.beta2)),w=_e(y,n),I=_e(b,s);p.assign(y),m.assign(b);const T=be(X(_e(w,be(Sn(I),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&qe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&qe(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(ii(this.beta1,this.iterations_+1)),this.accBeta2.assign(ii(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}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)}}Ah.className="Adam",ge(Ah);class vh extends sr{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:et(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:et(a).variable(c)});const u=Array.isArray(e)?e[o].tensor:e[i];if(u==null)return;const p=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(p,this.beta1),X(u,1-this.beta1)),b=X(m,this.beta2),w=rn(u),I=Us(b,w);p.assign(y),m.assign(I);const T=be(X(_e(s,n),_e(y,be(I,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&qe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&qe(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)}}vh.className="Adamax",ge(vh);class Za extends sr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Rn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Za.className="SGD",ge(Za);class Nh extends Za{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>et(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const u=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(u,this.m))),i):c=be(X(this.c,u),i),o.assign(u),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&qe(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Nh.className="Momentum",ge(Nh);class Ch extends sr{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>et(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>et(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;ee(()=>{const p=be(X(c,this.decay),X(Lt(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,y=be(X(m,this.decay),X(a,1-this.decay)),b=_e(X(a,this.learningRate),Sn(Ce(p,be(Lt(y),this.epsilon)))),w=be(X(u,this.momentum),b);c.assign(p),m.assign(y),u.assign(w);const I=Ce(i,w);i.assign(I)}else{const m=be(X(c,this.decay),X(Lt(a),1-this.decay)),y=be(X(u,this.momentum),_e(X(a,this.learningRate),Sn(be(m,this.epsilon))));c.assign(m),u.assign(y);const b=Ce(i,y);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&qe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&qe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&qe(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}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)}}Ch.className="RMSProp",ge(Ch);class Bo{static sgd(e){return new Za(e)}static momentum(e,t,n=!1){return new Nh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Ch(e,t,n,s,i)}static adam(e=.001,t=.9,n=.999,s=null){return new Ah(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new xh(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,i=0){return new vh(e,t,n,s,i)}static adagrad(e,t=.1){return new Th(e,t)}}const Mo={sgd:Bo.sgd,momentum:Bo.momentum,adadelta:Bo.adadelta,adagrad:Bo.adagrad,rmsprop:Bo.rmsprop,adamax:Bo.adamax,adam:Bo.adam};const dM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Ip(){return new Promise(e=>dM(()=>e()))}function qb(e,t,n){const s=n*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[s,i]}function Rh(e,t,n,s=!0){let i=[];if(s)i=i.concat(t.slice(0)),i.push(e[0]/n),i=i.concat(e.slice(1));else{i=i.concat(e[0]);const o=t.length;for(let a=0;a<o;++a)i=i.concat([e[a+1]/t[a],t[a]]);i=i.concat(e.slice(o+1))}return i}function Oh(e,t,n=!0){const s=[];if(n){s.push(t);for(let i=t+1;i<e;++i)i<=2*t?(s.push(i),s.push(i-(t+1))):s.push(i)}else{const i=[],o=[];for(let a=1;a<e;++a)a>=t*2+1||a%2===1?o.push(a):i.push(a);s.push(...i),s.push(0),s.push(...o)}return s}function Eh(e,t,n,s=!0){const i=[];s?i.push(e[0]/n):i.push(e[0]*n);for(let o=1;o<e.length;++o)o<=t.length?s?i.push(t[o-1]*e[o]):i.push(e[o]/t[o-1]):i.push(e[o]);return i}function jb(e,t){const n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function Kb(e,t,n){const s=e.slice(0,1);for(let i=0;i<n;++i)s.push(e[i+1]-t[i][0]-t[i][1]);return s}const xp=1.7580993408473768,Tp=1.0507009873554805;const Xb=.3275911,Jb=.254829592,Zb=-.284496736,Qb=1.421413741,ew=-1.453152027,tw=1.061405429;function Qa(...e){C().getBool("IS_TEST")||console.warn(...e)}function pM(...e){C().getBool("IS_TEST")||console.log(...e)}function ir(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(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function ev(e){const t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function tv(e){const t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let i=0;i<e.length;i+=4)n[Math.floor(i/4)]=e[i],s[Math.floor(i/4)]=e[i+1];return{real:n,imag:s}}function nv(e){const t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let i=2;i<e.length;i+=4)n[Math.floor(i/4)]=e[i],s[Math.floor(i/4)]=e[i+1];return{real:n,imag:s}}function nw(e,t){const n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function sv(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function iv(e,t){const n=new Float32Array(e/2),s=new Float32Array(e/2);for(let i=0;i<Math.ceil(e/2);i++){const o=(t?2:-2)*Math.PI*(i/e);n[i]=Math.cos(o),s[i]=Math.sin(o)}return{real:n,imag:s}}function rv(e,t,n){const s=(n?2:-2)*Math.PI*(e/t),i=Math.cos(s),o=Math.sin(s);return{real:i,imag:o}}function ov(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();const s=ct(e.shape),i=ve(e,"float32"),o=n.complex(i,s);return s.dispose(),i.dispose(),o}if(!xy(e.dtype,t))return V.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){const s=n.real(e),i=ve(s,t);return s.dispose(),i}if(t==="int32")return n.int(e);if(t==="bool"){const s=Ne(0,e.dtype),i=n.notEqual(e,s);return s.dispose(),i}else throw new Error(`Error in Cast: failed to cast ${e.dtype} to ${t}`)}function av(e,t){return V.makeTensorFromDataId(e.dataId,t,e.dtype)}function sw(e,t,n){const s=(t-e)/(n-1),i=Ea(n,"float32");i[0]=e;for(let o=1;o<i.length;o++)i[o]=i[o-1]+s;return rs(i,"float32")}var iw=Object.freeze({__proto__:null,slice_util:KT,segment_util:oW,castTensor:ov,reshapeTensor:av,linspaceImpl:sw,upcastType:Cn,axesAreInnerMostDims:ib,combineLocations:tA,computeOutAndReduceShapes:On,expandShapeToKeepDim:En,assertAxesAreInnerMostDims:ss,getAxesPermutation:_n,getUndoAxesPermutation:eh,getInnerMostAxes:Is,getBroadcastDims:Eo,getReductionAxes:an,assertAndGetBroadcastShape:nt,assertParamsConsistent:mb,computeOutShape:Ur,computeDilation2DInfo:zd,computePool2DInfo:Wn,computePool3DInfo:sh,computeConv2DInfo:Oi,computeConv3DInfo:ih,computeDefaultPad:ub,tupleValuesAreOne:$r,eitherStridesOrDilationsAreOne:on,convertConv2DDataFormat:rh,getFusedDyActivation:mp,getFusedBiasGradient:fp,applyActivation:gp,shouldFuse:yp,PARALLELIZE_THRESHOLD:xb,computeOptimalWindowSize:uh,getImageCenter:qb,getReshaped:Rh,getPermuted:Oh,getReshapedPermuted:Eh,getSliceBeginCoords:jb,getSliceSize:Kb,prepareAndValidate:Fd,validateUpdateShape:qy,validateInput:jy,calculateShapes:Ua,SELU_SCALEALPHA:xp,SELU_SCALE:Tp,ERF_P:Xb,ERF_A1:Jb,ERF_A2:Zb,ERF_A3:Qb,ERF_A4:ew,ERF_A5:tw,warn:Qa,log:pM,mergeRealAndImagArrays:ir,splitRealAndImagArrays:ev,complexWithEvenIndex:tv,complexWithOddIndex:nv,getComplexWithIndex:nw,assignToTypedArray:sv,exponents:iv,exponent:rv,prepareSplitSize:IA});function 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Ov={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Ev(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"initializer")}function Vt(e){return cw(e)}function Ft(e){if(typeof e=="string"){const t=e in Ov?Ov[e]:e;if(t==="GlorotNormal")return new Dp;if(t==="GlorotUniform")return new Ep;if(t==="HeNormal")return new kp;if(t==="HeUniform")return new Fp;if(t==="LeCunNormal")return new _p;if(t==="LeCunUniform")return new Wp;{const n={};return n.className=t,n.config={},Ev(n)}}else return e instanceof Ps?e:Ev(e)}function Mz(){return new Lw}function Pz(){return new Op}function zz(e){return new Sw(e)}function Gz(e){return new Iw(e)}function Vz(e){return new xw(e)}function Hz(e){return new Tw(e)}function Yz(e){return new Aw(e)}function qz(e){return new Zn(e)}function jz(e){return new Ep(e)}function Kz(e){return new Dp(e)}function Xz(e){return new kp(e)}function Jz(e){return new Fp(e)}function Zz(e){return new _p(e)}function Qz(e){return new Wp(e)}function e3(e){return new vw(e)}var t3=Object.freeze({__proto__:null,zeros:Mz,ones:Pz,constant:zz,randomUniform:Gz,randomNormal:Vz,truncatedNormal:Hz,identity:Yz,varianceScaling:qz,glorotUniform:jz,glorotNormal:Kz,heNormal:Xz,heUniform:Jz,leCunNormal:Zz,leCunUniform:Qz,orthogonal:e3});let n3=0;function Dv(){return n3++}const $p={};function Up(e=""){return e in $p||($p[e]=0),$p[e]+=1,e+$p[e].toString()}function Nw(e){return Array.isArray(e)&&Array.isArray(e[0])}function Bp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Xe(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function 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trainable(e){this.trainable_=e,this.val.trainable=e}}function s3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function QQ(e,t,n,s){return new ci(e,t,n,!0,s)}function eee(e,t,n){return new ci(ct(e),t,n)}function tee(e,t,n){return new ci(et(e),t,n)}function nee(e,t,n){const s=si(e);return new ci(s,t,n)}function see(e,t,n){const s=Dn(e);return new ci(s,t,n)}function iee(e,t,n){return new ci(Kd(e),t,n)}function ree(e,t,n,s,i,o="randomUniform"){return new ci($o(e,t,n,s),s,o)}function oee(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Ge(`randomNormal does not support dType ${s}.`);return new ci(Ih(e,t,n,s,i),s,o)}function aee(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Ge(`randomNormalVariable does not support dType ${s}.`);return new ci(Ob(e,t,n,s,i),s,o)}function cee(e,t){return e.write(t)}function lee(e,t){return e.write(be(e.read(),t))}function hee(e,t){return e.write(Ce(e.read(),t))}function Cw(e){return e.map(t=>t.read())}function Rw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function uee(e,t){const n=t.map(i=>i.read()),s=Ab(e,n);return t.map(i=>s.grads[i.name])}class fn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class li{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=Dv(),o!=null&&(this.originalName=xv(o),this.name=Tv(this.originalName)),this.rank=t.length}}let i3=0;class Pp{constructor(e,t){this.callArgs=t,this.id=i3++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let r3=0;class lt extends No{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=r3++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=or(n)+"_"+Up(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new oi(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new rr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new rr(`Layer ${this.name} is not connected, no input to return.`);return Jn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new rr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new rr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Jn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Nt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const u=Number(c),p=i.axes[c],m=u>=0?a[u]:a[a.length+u];if(p!=null&&[p,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${p} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],u=s.shape[a];if(c!=null&&u!=null&&c!==u)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Nt(e);let s=!0;for(const o of n)if(!(o instanceof li)){s=!1;break}let i=!0;for(const o of n)if(o instanceof li){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Go(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Nt(e))o.push(a.shape);this.build(Jn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Nt(o),c=[];for(let u of a)n.indexOf(u)!==-1&&(u=u.clone()),c.push(u);if(o=Jn(c),this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=o3(e),a=this.computeOutputShape(o);let c;const u=a3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((p,m)=>new li(u,p,this,Nt(e),t,this.name,m)):c=new li(u,a,this,Nt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new rr(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new rr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new oi(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Mp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Cw(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=Cw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}Rw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),u=new ci(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(u.read())),o==null&&(o=!0),o?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Nt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),i=Bp(i),o=Bp(o);const u=[],p=[],m=[];for(const y of c)u.push(y.sourceLayer),p.push(y.nodeIndex),m.push(y.tensorIndex);new Pp({outboundLayer:this,inboundLayers:u,nodeIndices:p,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function o3(e){e=Nt(e);const t=[];for(const n of e)t.push(n.shape);return Jn(t)}function a3(e){return"float32"}function Fv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],u=s.nodeIndices[o],p=Fv(a,c,u);for(const m of p)i.indexOf(m)===-1&&i.push(m)}return i}}}class sc extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Up("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new li(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}sc.className="InputLayer",ge(sc);function _v(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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n=[],s={};if(e.length===1){const i=Zv(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Zv(o,t);for(const u of a)i.has(u.name)||(n.push(u),i.add(u.name));for(const u in c)s[u]==null&&(s[u]=new Set),c[u].forEach(p=>s[u].add(p))}}return{sorted:n,recipientCounts:_3(s)}}function _3(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Zv(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const u=a[a.length-1]===o.length-1;if(c.inputs.length===0||u)o.pop(),s.push(c),n.add(c.name),u&&a.pop();else{a.push(o.length-1);for(const p of c.inputs){if(i[p.name]==null&&(i[p.name]=new Set),i[p.name].add(c.name),n.has(p.name))continue;o.push(p)}}}return{sorted:s,recipientMap:i}}function W3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class Ui extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Up(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Hr(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Hr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;vs(D===0,"input layer has >1 nodes"),vs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof sc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const U=F.inboundNodes[_];if(D.indexOf(U)!==-1)throw new oi(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf(U)!==-1)return;this.containerNodes.add(Ui.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf(U)===-1&&D.push(U);const Y=U.inboundLayers.length;for(let q=0;q<Y;q++){const J=U.inputTensors[q],oe=U.inboundLayers[q],ce=U.nodeIndices[q],ue=U.tensorIndices[q];c(J,E,D,oe,ce,ue)}for(E.push(U);D.indexOf(U)>=0;)D.splice(D.indexOf(U),1);a.push(U)},u=[],p=[];for(const N of this.outputs)c(N,u,p);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],U=_.inboundNodes[B],Y=t[U.id]==null?0:t[U.id];t[U.id]=Math.max(E+1,Y),n[U.id]=U}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(vp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof Ui&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(vp);const I=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(I.indexOf(F)===-1)throw new oi(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)I.push(F);T.push(D.name)}}this.nodesByDepth=y;const v=this.layers.map(N=>N.name);for(const N of v){const E=v.filter(D=>D===N).length;if(E!==1)throw new oi(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(v))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),u=c.slice(0,-2).concat([c[c.length-1]]);a=u.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}Rw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Xp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=$w(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Nt(e);const n=new Ho;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Ph(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Nt(e);let n;return t==null?n=Po(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Bp(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],u=t[a],p=c.name+"_0_0";n[p]=u}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(vp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const u of c){const p=u.outboundLayer;if(this.inputLayers.map(I=>I.id).indexOf(p.id)!==-1)continue;const m=[];for(let I=0;I<u.inboundLayers.length;I++){const T=u.inboundLayers[I],v=u.nodeIndices[I],N=u.tensorIndices[I],E=`${T.name}_${v}_${N}`,D=n[E];m.push(D)}const y=p.computeOutputShape(Jn(m)),b=Bp(y),w=p.inboundNodes.indexOf(u);for(let I=0;I<b.length;I++){const T=`${p.name}_${w}_${I}`;n[T]=b[I]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],u=this.outputLayersNodeIndices[a],p=this.outputLayersTensorIndices[a],m=`${c.name}_${u}_${p}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];vs(c in n),i.push(n[c])}return Jn(i)}runInternalGraph(e,t){t==null&&(t=Po(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const u=this.inputs[c],p=e[c],m=t[c];n[u.id]=[p,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(vp);for(const c of s){const u=this.nodesByDepth[c];for(const p of u){const m=p.outboundLayer,y=p.inputTensors,b=p.outputTensors,w=new Array;for(const I of y)I.id in n&&w.push(n[I.id]);if(w.length===y.length){let I={},T,v,N,E;if(p.callArgs!=null&&(I=p.callArgs),w.length===1){const[D,F]=w[0];I.mask==null&&(I.mask=F),N=Nt(m.call(D,I)),E=Nt(m.computeMask(D,F)),T=[D],v=[F]}else T=w.map(D=>D[0]),v=w.map(D=>D[1]),I.mask==null&&(I.mask=v),N=Nt(m.call(T,I)),E=Nt(m.computeMask(T,v));if(m.activityRegularizer)throw new Ge("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){vs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[u,p]=n[c.id];a.push(u.shape),i.push(u),o.push(p)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Ui?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Ui.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Ui.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),u=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=Ui.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(I){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const I=[];for(let T=0;T<y.inboundLayers.length;T++){const v=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=Ui.nodeKey(v,N);let F=t[D];F==null&&(F=0),I.push([v.name,F,E,w])}u.push(I)}}}const p={};p.name=o.name,p.className=a,p.config=c,p.inboundNodes=u,n.push(p)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],u=Ui.nodeKey(a,c);if(!this.containerNodes.has(u))continue;let p=t[u];p==null&&(p=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,p,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],u=Ui.nodeKey(a,c);if(!this.containerNodes.has(u))continue;let p=t[u];p==null&&(p=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,p,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,v){T.name in o?o[T.name].push(v):o[T.name]=[v]}function c(T,v){const N=[];let E;for(const D of v){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,v);return}const U=i[F];if(U.inboundNodes.length<=_){a(T,v);return}const Y=U.inboundNodes[_];N.push(Y.outputTensors[B])}N.length>0&&T.apply(Jn(N),E)}function u(T){const v=T.name,N=hi(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[v]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const p=t.name,m=t.layers;for(const T of m)u(T);for(;!pz(o);)for(const T of m){const v=i[T.name];if(v.name in o){const N=o[v.name];delete o[v.name];for(const E of N)c(v,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const v=T[0],N=T[1],E=T[2];vs(v in i);const D=i[v],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const I=t.outputLayers;for(const T of I){const v=T[0],N=T[1],E=T[2];vs(v in i);const D=i[v],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:p})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Qv(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function eN(e,t){return Qv(e,t,"classWeight")}function Aee(e,t){return Qv(e,t,"sampleWeight")}async function tN(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());qe(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${c} exists in the data but not in classWeight`);a.push(n[c])}),rs(a,"float32")}else return null}function $3(e,t){return X(e,t)}const U3=32;function nN(e,t){let n,s;const i=t;n=i.xs,s=i.ys,k(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);const o=sN("input",e.inputNames,n),a=sN("output",e.outputNames,s),c=o[0].shape[0];k(o.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k(a.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<o.length;u++)k(o[u].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${o[u].shape[0]}; expected ${c} based on input ${e.inputNames[0]}.`);for(let u=0;u<a.length;u++)k(a[u].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${a[u].shape[0]}; expected ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function sN(e,t,n){if(n instanceof Q)return[n];if(Array.isArray(n))return k(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{const s=[];for(const i of t){if(n[i]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function B3(e){if(e.length===3)throw new Ge("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function M3(e,t,n){const s=n.batchesPerEpoch!=null;if(k(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=n.validationData!=null;let o,a;if(i)if(iN(n.validationData))k(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{const v=B3(n.validationData);o=v.xs,a=v.ys}const c=e.makeTrainFunction(),u=e.getDedupedMetricsNames();let p;i?p=u.slice().concat(u.map(v=>"val_"+v)):p=u.slice();const m=Pv(n.callbacks,n.yieldEvery),y=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=zv(m,y,n.epochs,null,null,P3(t,n),null,i,p);b.setModel(e),e.history=w,await b.onTrainBegin(),e.stopTraining_=!1;let I=n.initialEpoch==null?0:n.initialEpoch,T=await t.iterator();for(;I<n.epochs;){const v={};await b.onEpochBegin(I);let N=0,E=0;for(s||(T=await t.iterator());s?N<n.batchesPerEpoch:!0;){const D=await T.next();if(s&&D.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${N} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===Gp?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=aN(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=aN(t,this.feedOutputNames,i,!1,"target"),q3(e,t,null),j3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){const p=eN(s,this.outputNames);u=[];for(let m=0;m<p.length;++m)u.push(await tN(c[m],null,p[m]))}return[a,c,u]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new Ge("Verbose mode is not implemented yet.");if(i!=null)throw new Ge("steps mode in testLoop() is not implemented yet");{const c=Pw(o,n),u=rs(ai(0,o));for(let p=0;p<c.length;++p){const m=c[p][0],y=c[p][1],b=Vo(u,m,y-m),w=Mw(t,b),I=e(w);if(p===0)for(let T=0;T<I.length;++T)a.push(Ne(0));for(let T=0;T<I.length;++T){const v=I[T];a[T]=be(a[T],X(y-m,v))}}for(let p=0;p<a.length;++p)a[p]=_e(a[p],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(gv(e,s)>1){const o=gv(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let I=0;I<this.inputs.length;++I)m.push({key:this.inputs[I],value:n[I]});const y=new Ho(m),b=Ph(this.outputs,y,{training:!0});let w;for(let I=0;I<this.lossFunctions.length;++I){const T=this.lossFunctions[I];let v=T(s[I],b[I]);i[I]!=null&&(v=$3(v,i[I]));const N=zt(v);t.push(N),I===0?w=v:w=be(w,v)}for(let I=0;I<this.metricsTensors.length;++I){let T;if(this.outputs.length>1&&I<this.outputs.length)T=t[I];else{const v=this.metricsTensors[I][0],N=this.metricsTensors[I][1];T=zt(v(s[N],b[N]))}Rn(T),o.push(T)}return w=zt(w),this.calculateLosses().forEach(I=>{w=be(w,I)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),u=!0,p=this.optimizer_.minimize(a,u,c);return[p].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let u=0;u<this.inputs.length;++u)o.push({key:this.inputs[u],value:s[u]});const a=new Ho(o),c=Ph(this.outputs,a);for(let u=0;u<this.lossFunctions.length;++u){const p=this.lossFunctions[u],m=zt(p(i[u],c[u]));u===0?n=m:n=be(n,m),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){const p=this.metricsTensors[u][0],m=this.metricsTensors[u][1],y=zt(p(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return H3(this,e,t,n)}async fitDataset(e,t){return M3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const u of a){const p=await u.data();c.push(p[0])}return qe(a),Jn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Bd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Bd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=or(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>or(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=or(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[or(jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>or(jp(e)));{const e={};for(const t in this.metrics)e[t]=or(jp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Mh(e.optimizer_config),n=hi(t);let s;if(typeof e.loss=="string")s=zo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>zo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=zo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>zo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=zo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const u=Uy(e);if(u.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new j(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await Wy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:X3,generatedBy:`TensorFlow.js tfjs-layers v${Xp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const u="optimizer",{data:p,specs:m}=await Wy(await this.optimizer.getWeights(),u);n.specs.push(...m),n.data=Od([n.data,p])}if(this.userDefinedMetadata!=null){const u=!0;Kv(this.userDefinedMetadata,this.name,u),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Kv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}cr.className="Model",ge(cr);class lN extends cr{}lN.className="Functional",ge(lN);async function J3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Mh(n),i=hi(s,t);if(e.weightsManifest!=null){const o=await _T(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),qe(o)}return i}async function Z3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=By(e,t);if(n.length===0)n.push(kd(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Q3(e,void 0,t)}async function Q3(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=hi(Mh(i),t,a),u=s.trainingConfig;if(u!=null&&c.loadTrainingConfig(u),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:p,optimizerWeights:m}=eG(s.weightData,s.weightSpecs);c.loadWeights(p,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),qe(p),qe(m.map(y=>y.tensor))}return c}function eG(e,t){const n=Rd(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class oc extends cr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Up("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof oc||e instanceof cr;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=_v({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=Fv(this.outputs[0])}this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Po(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(It(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new cr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof oc))throw new Ge(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const u=void 0,p=hi(c,u,s);s&&p.setFastWeightInitDuringBuild(!0),a.add(p)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}oc.className="Sequential",ge(oc);function tG(e){return new cr(e)}function nG(e){return new oc(e)}function sG(e,t){return t==null&&(t={}),Z3(e,t)}function hN(e){return _v(e)}function iG(e,t){zs.registerCallbackConstructor(e,t)}class cs extends No{getConfig(){return{}}}class uN extends cs{apply(e,t=1){return Dz(e,t)}}uN.className="elu",ge(uN);class dN extends cs{apply(e){return rp(e)}}dN.className="selu",ge(dN);class pN extends cs{apply(e){return Fi(e)}}pN.className="relu",ge(pN);class mN extends cs{apply(e){return ee(()=>ko(6,Fi(e)))}}mN.className="relu6",ge(mN);class fN extends cs{apply(e){return e}}fN.className="linear",ge(fN);class gN extends cs{apply(e){return Ei(e)}}gN.className="sigmoid",ge(gN);class yN extends cs{apply(e){return Fz(e)}}yN.className="hardSigmoid",ge(yN);class bN extends cs{apply(e){return Va(e)}}bN.className="softplus",ge(bN);class wN extends cs{apply(e){return kz(e)}}wN.className="softsign",ge(wN);class LN extends cs{apply(e){return Ma(e)}}LN.className="tanh",ge(LN);class Gw extends cs{apply(e,t=-1){return Uo(e,t)}}Gw.className="softmax",ge(Gw);class SN extends cs{apply(e,t=-1){return Qd(e,t)}}SN.className="logSoftmax",ge(SN);class IN extends cs{apply(e,t=1){return ee(()=>Ei(e.mul(t)).mul(e))}}IN.className="swish",ge(IN);function Xr(e){return e.getClassName()}function Vw(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"activation")}function Jr(e){if(e==null){const t={};return t.className="linear",t.config={},Vw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Vw(t)}else return e instanceof cs?e:Vw(e)}function Hw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class xN extends No{}class Gh extends xN{constructor(e){super();Hw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,rn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,$h(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Gh.className="L1L2",ge(Gh);function rG(e){return Hw(e),new Gh({l1:e!=null?e.l1:null,l2:0})}function oG(e){return Hw(e),new Gh({l2:e!=null?e.l2:null,l1:0})}const TN={l1l2:"L1L2"};function xt(e){return cw(e)}function AN(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in TN?TN[e]:e,n={className:t,config:{}};return AN(n)}else return e instanceof xN?e:AN(e)}class Yw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Xe(e);let n=Fi(e);return this.maxValue!=null&&(n=jn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Yw.className="ReLU",ge(Yw);class qw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Xd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}qw.className="LeakyReLU",ge(qw);class jw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=hn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=It(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new fn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Xe(e),gh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Vt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:ln(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}jw.className="PReLU",ge(jw);class Kw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ge(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Do(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Kw.className="ELU",ge(Kw);class Xw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=Xe(e);return n.mul(_h(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Xw.className="ThresholdedReLU",ge(Xw);class Jw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Xe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="Softmax",ge(Jw);function ac(e,t,n){if(typeof e=="number")return Po(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!vz(i))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function ui(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function Jp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+qr([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function Zw(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Pe(e,[0,2,3,1]):e))}function vN(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Pe(e,[0,2,3,4,1]):e))}function NN(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Pe(e,[0,2,1])),i==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=Hd(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=$i(c,n)),c})}function vee(e,t,n=1,s="valid",i,o=1){return ee(()=>(Gt(i),NN(e,t,null,n,s,i,o)))}function Nee(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Gt(i),Qw(e,t,null,n,s,i,o)))}function Qw(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zw(e,o);if(i==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Hb({x:u,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(u=Pe(u,[0,3,1,2])),u})}function Cee(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Gt(i),CN(e,t,null,n,s,i,o)))}function CN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=vN(e,o);if(i==="causal")throw new Ge("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=yb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=$i(c,n)),o==="channelsFirst"&&(c=Pe(c,[0,4,1,2,3])),c})}class eL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",eL.verifyArgs(t),this.rank=e,mn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ge(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ac(t.kernelSize,e,"kernelSize"),this.strides=ac(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ns(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Jr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=hn(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=ac(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(vs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Xr(this.activation),useBias:this.useBias,biasInitializer:Vt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Vh extends eL{constructor(e,t){super(e,t);this.kernel=null,Vh.verifyArgs(t),this.filters=t.filters,mn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=hn(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=It(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n;const s=this.bias==null?null:this.bias.read(),i=bv(this.activation.getClassName());if(i!=null&&this.rank===2)n=Qw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=NN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Qw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=CN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ge("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=It(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=ui(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Vt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:ln(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Hh extends Vh{constructor(e){super(2,e);Hh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Hh.className="Conv2D",ge(Hh);class Zp extends Vh{constructor(e){super(3,e);Zp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Zp.className="Conv3D",ge(Zp);class tL extends Hh{constructor(e){super(e);if(this.inputSpec=[new fn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=It(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new fn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=Xe(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],u=s[a],p=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=Jp(c,y,p,this.padding),I=Jp(u,b,m,this.padding),T=[i,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Pe(n,[0,2,3,1]));let v=Yd(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Pe(v,[0,3,1,2])),this.bias!=null&&(v=$i(v,this.bias.read(),this.dataFormat)),this.activation!=null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=It(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Jp(t[s],c,o,this.padding),t[i]=Jp(t[i],u,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}tL.className="Conv2DTranspose",ge(tL);class RN extends Vh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=hn(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=hn(t.pointwiseConstraint)}build(e){if(e=It(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new fn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n;if(this.rank===1)throw new Ge("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Pe(e,[0,2,3,1])),n=Fb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=$i(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Pe(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.pointwiseInitializer=Vt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseConstraint),e.pointwiseConstraint=ln(this.pointwiseConstraint),e}}RN.className="SeparableConv";class nL extends RN{constructor(e){super(2,e)}}nL.className="SeparableConv2D",ge(nL);class Qp extends Vh{constructor(e){super(1,e);Qp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Qp.className="Conv1D",ge(Qp);class sL extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=Xe(e),this.dataFormat==="channelsLast"){const n=Cp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Cp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Cp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Cp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}sL.className="Cropping2D",ge(sL);class iL extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=Xe(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Pe(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Pe(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}iL.className="UpSampling2D",ge(iL);function aG(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ri()),Gt(i);let a=Zw(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=Oo(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Pe(a,[0,3,1,2])),a})}class rL extends eL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=hn(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=It(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n=aG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=$i(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=It(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=ui(t,this.kernelSize[0],this.padding,this.strides[0]),o=ui(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseRegularizer),e}}rL.className="DepthwiseConv2D",ge(rL);function ON(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function EN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const u=t.shape.length;if(u<3)throw new j(`Input should be at least 3D, but is ${u}D.`);const p=[1,0].concat(ai(2,u));if(t=Pe(t,p),o!=null)throw new Ge("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===u-1&&(i=Kn(i,-1)),i=Pe(i,p)),s&&(t=As(t,0),i!=null&&(i=As(i,0)));const m=[];let y,b=n;const w=t.shape[0],I=_i(t);let T;i!=null&&(T=_i(i));for(let N=0;N<w;++N){const E=I[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=Dn(_).sub(_),U=D[0].mul(_).add(b[0].mul(B)),Y=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:U,newStates:Y}});y=F.output,b=F.newStates}c&&m.push(y)}let v;if(c){const N=1;v=as(m,N)}return[y,v,b]})}class Bi extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new nm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new fn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ai(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Nw(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new Ge("Constants support is not implemented in RNN yet.");Nw(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new fn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new Ge("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new fn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new rr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)qe(this.states_),this.keptStates!=null&&(qe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):qe(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Rn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=ON(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const u of n)this.stateSpec.push(new fn({shape:u.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof li;if(c){const u=[e].concat(o),p=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=p;const y=super.apply(u,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Xe(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,I)=>{const T=this.cell.call([w].concat(I),a);return[T[0],T.slice(1)]},u=EN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),p=u[0],m=u[1],y=u[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:p;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Wh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?bw(t,[1,n]):t):this.cell.stateSize>1?[bw(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===Bi.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=hi(s,n);return new e(Object.assign(t,{cell:i}))}}Bi.className="RNN",ge(Bi);class cc extends lt{}class em extends cc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=nc([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=It(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Wi(X(e,o),this.kernel.read()):i=Wi(e,this.kernel.read()),this.bias!=null&&(i=$i(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Wi(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}em.className="SimpleRNNCell",ge(em);class oL extends Bi{constructor(e){e.cell=new em(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}oL.className="SimpleRNN",ge(oL);class tm extends cc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Jr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=nc([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=It(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,u;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let p=Wi(e,this.kernel.read());this.useBias&&(p=$i(p,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=os(m,[2*this.units,this.units],m.rank-1),w=Wi(s,y),[I,T,v]=os(p,3,p.rank-1),[N,E]=os(w,2,w.rank-1);a=this.recurrentActivation.apply(be(I,N)),c=this.recurrentActivation.apply(be(T,E));const D=Wi(X(c,s),b);u=this.activation.apply(be(v,D));const F=be(X(a,s),X(be(1,Pt(a)),u));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),recurrentActivation:Xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}tm.className="GRUCell",ge(tm);class aL extends Bi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new tm(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}aL.className="GRU",ge(aL);class Yh extends cc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Jr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=nc([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=It(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Ps{apply(c,u){const p=i.apply([o]),m=new Op().apply([o]),y=i.apply([o*2]);return Nv(Nv(p,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,u,p,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Wi(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Wi(s,this.recurrentKernel.read())),this.useBias&&(y=$i(y,this.bias.read()));const[b,w,I,T]=os(y,4,y.rank-1);c=this.recurrentActivation.apply(b),u=this.recurrentActivation.apply(w),p=be(X(u,i),X(c,this.activation.apply(I))),m=this.recurrentActivation.apply(T);const v=X(m,this.activation.apply(p));return[v,v,p]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),recurrentActivation:Xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Yh.className="LSTMCell",ge(Yh);class cL extends Bi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Yh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}cL.className="LSTM",ge(cL);class nm extends cc{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){Nw(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Go(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(hi(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return Cw(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}Rw(t)}}nm.className="StackedRNNCells",ge(nm);function Zr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>Rv(t(),n),a=()=>Uh(o,t,s);if(!i||i<=1)return Rn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(u=>Rn(u.clone()))}var cG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class Ree extends cc{}class DN extends Bi{constructor(e){if(e.unroll)throw new Ge("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ge("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new fn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new rr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)qe(this.states_),this.keptStates!=null&&(qe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):qe(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],u=i;if(!ot(c.shape,u))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Rn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",u=e[c?3:2],p=e[c?4:3],m=ui(u,s[0],i,o[0],a[0]),y=ui(p,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}DN.className="ConvRNN2D";class sm extends Yh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,mn(this.filters,"filters"),this.kernelSize=ac(n,2,"kernelSize"),this.kernelSize.forEach(c=>mn(c,"kernelSize")),this.strides=ac(s||1,2,"strides"),this.strides.forEach(c=>mn(c,"strides")),this.padding=i||"valid",Ns(this.padding),this.dataFormat=o||"channelsLast",Gt(this.dataFormat),this.dilationRate=ac(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>mn(c,"dilationRate"))}build(e){var t;e=It(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const u=this.biasInitializer,p=this.filters;c=new(t=class extends Ps{apply(y,b){const w=u.apply([p]),I=si([p]),T=u.apply([p*2]);return yw([w,I,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,u=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let p=u(s,c,0),m=u(s,c,1),y=u(s,c,2),b=u(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let I=u(i,w,0),T=u(i,w,1),v=u(i,w,2),N=u(i,w,3);const E=3,[D,F,_,B]=os(this.kernel.read(),a,E),[U,Y,q,J]=this.useBias?os(this.bias.read(),a):[null,null,null,null];p=this.inputConv(p,D,U,this.padding),m=this.inputConv(m,F,Y,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[oe,ce,ue,he]=os(this.recurrentKernel.read(),a,E);I=this.recurrentConv(I,oe),T=this.recurrentConv(T,ce),v=this.recurrentConv(v,ue),N=this.recurrentConv(N,he);const pe=this.recurrentActivation.apply(be(p,I)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(pe,this.activation.apply(be(y,v)))),me=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[me,me,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=cG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=er(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?$i(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return er(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}sm.className="ConvLSTM2DCell",ge(sm);class lL extends DN{constructor(e){const t=new sm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}lL.className="ConvLSTM2D",ge(lL);class im extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Uh(()=>Rv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}im.className="Dropout",ge(im);class hL extends im{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}hL.className="SpatialDropout1D",ge(hL);class uL extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=hn(e.kernelConstraint),this.biasConstraint=hn(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=It(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=It(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=bv(this.activation.getClassName());let i;return s!=null?i=Wi(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Wi(n,this.kernel.read()),this.bias!=null&&(i=$i(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Xr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}uL.className="Dense",ge(uL);class dL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=It(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Yr(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Xe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return Ez(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}dL.className="Flatten",ge(dL);class pL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Jr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);return this.activation.apply(n)})}getConfig(){const e={activation:Xr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}pL.className="Activation",ge(pL);class mL extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=Xe(e),Rz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}mL.className="RepeatVector",ge(mL);class fL extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const u=s[c];if(this.isUnknown(u))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=u}const a=Yr(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}fL.className="Reshape",ge(fL);class gL extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=ai(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new fn({ndim:this.dims.length+1})]}computeOutputShape(e){e=It(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Pe(Xe(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}gL.className="Permute",ge(gL);class yL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=Xe(e),s=-1;return th(Pr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=-1,i=!0,o=th(Pr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}yL.className="Masking",ge(yL);class bL extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Nt(e.inputLength))}this.inputDim=e.inputDim,mn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,mn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=hn(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=Xe(e),Pr(e,et(e))):null)}computeOutputShape(e){if(e=It(e),this.inputLength==null)return[...e,this.outputDim];const t=Nt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Xe(e);n.dtype!=="int32"&&(n=_h(n,"int32"));const s=Cv(this.embeddings.read(),n.as1D());return s.reshape(It(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Vt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:ln(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}bL.className="Embedding",ge(bL);class qo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ge}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[It(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=Hr(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Hr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=qr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Wh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const u=c.rank;if(u==null){const p=c.shape,m=p[0],y=p.slice(1).concat([m]);let b=c.reshape([m].concat(Yr(p.slice(1))));b=Pe(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(u>1){const p=ai(1,u).concat([0]);n.push(Pe(c,p)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,u=c.length,p=c[u-1],m=[p].concat(c.slice(0,c.length-1));o=Pe(o.reshape([-1,p]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(ai(0,a-1));o=Pe(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Hr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Kn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Bs(n,t[s]);return n})}}class qh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}qh.className="Add",ge(qh);function Oee(e){if(Array.isArray(e)){const t=new qh({});return t.apply(e)}else return new qh(e)}class jh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}jh.className="Multiply",ge(jh);function Eee(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}class Kh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}Kh.className="Average",ge(Kh);function Dee(e){if(Array.isArray(e)){const t=new Kh({});return t.apply(e)}else return new Kh(e)}class Xh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Us(t,e[n]);return t})}}Xh.className="Maximum",ge(Xh);function kee(e){if(Array.isArray(e)){const t=new Xh({});return t.apply(e)}else return new Xh(e)}class Jh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ko(t,e[n]);return t})}}Jh.className="Minimum",ge(Jh);function Fee(e){if(Array.isArray(e)){const t=new Jh({});return t.apply(e)}else return new Jh(e)}class Zh extends qo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>yw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(Dn(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Kn(t[o],-1)):s.push(t[o]);const i=Mt(s,this.axis);return Pd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Zh.className="Concatenate",ge(Zh);function _ee(e){if(Array.isArray(e)){const t=new Zh({});return t.apply(e)}else return new Zh(e)}function Qh(e,t){for(;e<0;)e+=t;return e}function lG(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ge("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ge("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const u=[];for(let p=0;p<a;++p)u.push(1);t=t.reshape(t.shape.concat(u))}else if(i>s){a=i-s;const u=[];for(let p=0;p<a;++p)u.push(1);e=e.reshape(e.shape.concat(u))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const u=o[0]!==e.shape.length-1,p=o[1]===t.shape.length-1;c=e.matMul(t,u,p)}if(a>0){let u;s>i?u=s+i-3:u=s-1;const p=[];for(let m=u;m<u+a;++m)p.push(m);c=c.squeeze(p)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class wL extends qo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Qh(i,e[o].shape.length)):s=[Qh(this.axes,t.shape.length),Qh(this.axes,n.shape.length)],this.normalize&&(t=zp(t,s[0]),n=zp(n,s[1])),lG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Qh(this.axes,e.length),Qh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}wL.className="Dot",ge(wL);class LL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=()=>Rp(n.shape,0,this.stddev).add(n),i=Uh(s,()=>n,t.training||!1);return i})}}LL.className="GaussianNoise",ge(LL);class SL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Rp(n.shape,1,i))};return Uh(s,()=>n,t.training||!1)}return n})}}SL.className="GaussianDropout",ge(SL);class IL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Xe(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Xe(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let u=tr($o(n),this.rate);u=_h(u,"float32");const p=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-p*c*this.rate,y=i.mul(u).add(u.add(-1).mul(c));return y.mul(p).add(m)};return Uh(s,()=>Xe(e),t.training||!1)}return e})}}IL.className="AlphaDropout",ge(IL);function eu(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=nA(e,t,n,s,i,o);else if(e.rank===3)a=sA(e,t,n,s,i,o);else if(e.rank===4)a=iA(e,t,n,s,i,o);else throw new Ge(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function hG(e,t,n,s,i=.001){return ee(()=>{const o=np(e,s),a=o.mean,c=o.variance,u=eu(e,a,c,n,t,i);return[u,a,c]})}function uG(e,t,n,s,i=.001){return ee(()=>{const o=np(e,s),a=o.mean,c=o.variance,u=[];for(const I of ai(0,e.rank))s.indexOf(I)!==-1?u.push(1):u.push(e.shape[I]);const p=a.reshape(u),m=c.reshape(u),y=t==null?null:t.reshape(u),b=n==null?null:n.reshape(u),w=eu(e,p,m,b,y,i);return[w,a,c]})}function dG(e,t,n,s,i=.001){return ot(s.slice().sort(),ai(0,e.rank-1))?hG(e,t,n,s,i):uG(e,t,n,s,i)}class xL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=hn(e.betaConstraint),this.gammaConstraint=hn(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=It(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new fn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=Xe(e),i=s.shape,o=i.length,a=ai(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const u=Po(1,o);u[c]=i[c];const p=a.slice();p.sort();const m=!ot(p,ai(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(u),E=this.movingVariance.read().reshape(u),D=this.center?this.beta.read().reshape(u):null,F=this.scale?this.gamma.read().reshape(u):null;return eu(s,N,E,D,F,this.epsilon)}else return eu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,I]=dG(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},v=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,I,this.momentum)};return v(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),movingMeanInitializer:Vt(this.movingMeanInitializer),movingVarianceInitializer:Vt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:ln(this.betaConstraint),gammaConstraint:ln(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}xL.className="BatchNormalization",ge(xL);class TL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=It(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==Hr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=Xe(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=np(n,this.axis,o);const u=Po(1,i);for(const I of this.axis)u[I]=s[I];const p=I=>I!=null&&I.shape.length!==i&&this.axis!==[i-1]?I.reshape(u):I;let m=p(this.gamma.read()),y=p(this.beta.read());const b=[],w=[];for(let I=0;I<i;++I)this.axis.indexOf(I)!==-1?(b.push(s[I]),w.push(1)):(b.push(1),w.push(s[I]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),eu(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}TL.className="LayerNormalization",ge(TL);function Wee(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return ki(e,n)})}function pG(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ri()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],ki(e,s)})}class AL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ri():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){e=It(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>pG(Xe(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}AL.className="ZeroPadding2D",ge(AL);function rm(e,t,n,s,i,o){return ee(()=>{Gt(i),Sv(o),Ns(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ri()),o==null&&(o="max"),e=Zw(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=mh(e,t,n,c):a=oh(e,t,n,c),i==="channelsFirst"&&(a=Pe(a,[0,3,1,2])),a})}function kN(e,t,n,s,i,o){return ee(()=>{Gt(i),Sv(o),Ns(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ri()),o==null&&(o="max"),e=vN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Nb(e,t,n,c):a=pb(e,t,n,c),i==="channelsFirst"&&(a=Pe(a,[0,4,1,2,3])),a})}class FN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(mn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ns(this.padding),this.inputSpec=[new fn({ndim:3})]}computeOutputShape(e){e=It(e);const t=ui(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Wh(Xe(e),2);const n=this.poolingFunction(Xe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class vL extends FN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"max")}}vL.className="MaxPooling1D",ge(vL);class NL extends FN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"avg")}}NL.className="AveragePooling1D",ge(NL);class _N extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ns(this.padding),this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){e=It(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ui(t,this.poolSize[0],this.padding,this.strides[0]),n=ui(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class CL extends _N{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"max")}}CL.className="MaxPooling2D",ge(CL);class RL extends _N{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"avg")}}RL.className="AveragePooling2D",ge(RL);class WN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ns(this.padding),this.inputSpec=[new fn({ndim:5})]}computeOutputShape(e){e=It(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ui(t,this.poolSize[0],this.padding,this.strides[0]),n=ui(n,this.poolSize[1],this.padding,this.strides[1]),s=ui(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class OL extends WN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),kN(e,t,n,s,i,"max")}}OL.className="MaxPooling3D",ge(OL);class EL extends WN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),kN(e,t,n,s,i,"avg")}}EL.className="AveragePooling3D",ge(EL);class $N extends lt{constructor(e){super(e);this.inputSpec=[new fn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ge}}class DL extends $N{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Xe(e);return zt(n,1)})}}DL.className="GlobalAveragePooling1D",ge(DL);class kL extends $N{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Xe(e);return Xn(n,1)})}}kL.className="GlobalMaxPooling1D",ge(kL);class UN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ge}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class FL extends UN{call(e,t){return ee(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}}FL.className="GlobalAveragePooling2D",ge(FL);class _L extends UN{call(e,t){return ee(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?Xn(n,[1,2]):Xn(n,[2,3])})}}_L.className="GlobalMaxPooling2D",ge(_L);class BN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=hi(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class WL extends BN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=It(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=It(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=Xe(e);const n=(o,a)=>{const c=Xe(this.layer.call(o,t));return[c,[]]},s=EN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}WL.className="TimeDistributed",ge(WL);function mG(e){ec(xz,"BidirectionalMergeMode",e)}const fG="concat";class $L extends BN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=hi(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=hi(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fG:e.mergeMode,mG(this.mergeMode),e.weights)throw new Ge("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):Jn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=ON(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const u=n.length;if(u%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const p=n.map(m=>new fn({shape:m.shape}));this.forwardLayer.stateSpec=p.slice(0,u/2),this.backwardLayer.stateSpec=p.slice(u/2),a.push(...p)}if(s!=null)throw new Ge("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof li;for(const u of o)if(u instanceof li!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const u=[e].concat(o),p=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=p;const y=super.apply(u,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=As(i,1));let a;return this.mergeMode==="concat"?a=yw([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Go(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Go(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=hi(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ge("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}$L.className="Bidirectional",ge($L);function gG(e){return new sc(e)}function yG(e){return new Kw(e)}function bG(e){return new Yw(e)}function wG(e){return new qw(e)}function LG(e){return new jw(e)}function SG(e){return new Jw(e)}function IG(e){return new Xw(e)}function xG(e){return new Qp(e)}function TG(e){return new Hh(e)}function AG(e){return new tL(e)}function 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aH=Object.freeze({__proto__:null,json:oH});const 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dH=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];var pH=Object.freeze({__proto__:null,json:dH});const mH=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var fH=Object.freeze({__proto__:null,json:mH});const 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yH=Object.freeze({__proto__:null,json:gH});const 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wH=Object.freeze({__proto__:null,json:bH});const LH=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var SH=Object.freeze({__proto__:null,json:LH});const 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xH=Object.freeze({__proto__:null,json:IH});const TH=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var AH=Object.freeze({__proto__:null,json:TH});const vH=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var NH=Object.freeze({__proto__:null,json:vH});const CH=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var 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zL(this.node.rawAttrs,e,t);if(n.b!=null)return GL(this.node.rawAttrs,e,t);if(n.shape!=null)return jL(this.node.rawAttrs,e,t);if(n.type!=null)return YL(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return KL(this.node.rawAttrs,e,t);if(n.list.s!=null)return XL(this.node.rawAttrs,e,t);if(n.list.shape!=null)return JL(this.node.rawAttrs,e,t);if(n.list.b!=null)return ZL(this.node.rawAttrs,e,t);if(n.list.type!=null)return qL(this.node.rawAttrs,e,t)}return t}}const DH=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[be(R("a",e,t,n),R("b",e,t,n))];case"AddN":return[eA(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[tp(R("a",e,t,n),R("b",e,t,n))];case"Mul":return[X(R("a",e,t,n),R("b",e,t,n))];case"RealDiv":case"Div":return[_e(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[Lb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Md(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Ce(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[ko(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[Us(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[ii(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[Sh(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Uee="arithmetic";const kH=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[rn(R("x",e,t,n))];case"Acos":return[nb(R("x",e,t,n))];case"Acosh":return[sb(R("x",e,t,n))];case"Asin":return[ob(R("x",e,t,n))];case"Asinh":return[ab(R("x",e,t,n))];case"Atan":return[cb(R("x",e,t,n))];case"Atan2":return[lb(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[hb(R("x",e,t,n))];case"Ceil":return[fb(R("x",e,t,n))];case"Complex":return[Ci(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[lh(R("x",e,t,n))];case"Cosh":return[qd(R("x",e,t,n))];case"Elu":return[Do(R("x",e,t,n))];case"Erf":return[Sb(R("x",e,t,n))];case"Exp":return[xs(R("x",e,t,n))];case"Expm1":return[Ib(R("x",e,t,n))];case"Floor":return[Pa(R("x",e,t,n))];case"Log":return[is(R("x",e,t,n))];case"Log1p":return[Jd(R("x",e,t,n))];case"Imag":return[Ga(R("x",e,t,n))];case"Neg":return[Pt(R("x",e,t,n))];case"Reciprocal":return[Eb(R("x",e,t,n))];case"Real":return[Fo(R("x",e,t,n))];case"Relu":return[Fi(R("x",e,t,n))];case"Round":return[kb(R("x",e,t,n))];case"Selu":return[rp(R("x",e,t,n))];case"Sigmoid":return[Ei(R("x",e,t,n))];case"Sin":return[op(R("x",e,t,n))];case"Sign":return[_b(R("x",e,t,n))];case"Sinh":return[ap(R("x",e,t,n))];case"Softplus":return[Va(R("x",e,t,n))];case"Sqrt":return[Sn(R("x",e,t,n))];case"Square":return[Lt(R("x",e,t,n))];case"Tanh":return[Ma(R("x",e,t,n))];case"Tan":return[Ub(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[jn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[ip(Qn(e.inputNames[0],t,n))];case"Prod":return[sp(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[Xd(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[gh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Bee="basic_math";function Gs(e,t,n=""){k(FH(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function FH(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}class _H{constructor(e,t,n,s,i,o,a){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=i,this.dynamicSize=o,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=Ne(0),Rn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const i=n===0?0:t.size/n,o=[];ee(()=>{t=K(t,[1,n,i]);for(let c=0;c<e.length;++c){const u=c===0?0:s[c-1],p=[0,u,0],m=[1,e[c],i];o[c]=K(st(t,p,m),this.elementShape)}return o});const a=[];for(let c=0;c<e.length;c++)a[c]=c;this.writeMany(a,o)}}class tu{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(i=>{if(n!==i.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${i.dtype}`);Gs(t,i.shape,"TensorList shape mismatch: "),Rn(i)}),this.idTensor=Ne(0),this.maxNumElements=s,Rn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new tu([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return Gs(e,this.elementShape,"TensorList shape mismatch: "),ee(()=>{const s=this.tensors.map(i=>K(i,e));return as(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const n=this.tensors.pop();return Gs(n.shape,e,"TensorList shape mismatch: "),K(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Gs(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Rn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return Gs(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Gs(this.elementShape,t.shape,"TensorList shape mismatch: "),Rn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return Gs(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?en([],[0].concat(this.elementShape)):ee(()=>{const s=e.map(i=>K(this.tensors[i],n));return as(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Gs(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?en([],[0].concat(this.elementShape)):ee(()=>{const n=this.tensors.map(s=>K(s,t));return Mt(n,0)})}}function WH(e,t,n){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);const i=e.shape.slice(1);Gs(i,t,"TensorList shape mismatch: ");const o=_i(e);return new tu(o,t,s)}function $H(e,t,n){return new tu([],e,t,n)}function UH(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const i=Math.max(...t);if(s!=null&&s!==-1&&i>=s)throw new Error(`Max index must be < array size (${i} vs. ${s})`);const o=new tu([],n,e.dtype,s),a=_i(e,0);return t.forEach((c,u)=>{o.setItem(c,a[u])}),o}function BH(e,t,n){let s=0;const i=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=ee(()=>{const u=[];e=K(e,[1,s,o]);for(let p=0;p<t.length;++p){const m=p===0?0:i[p-1],y=[0,m,0],b=[1,t[p],o];u[p]=K(st(e,y,b),n)}return e.dispose(),u}),c=new tu([],n,e.dtype,t.length);for(let u=0;u<a.length;u++)c.setItem(u,a[u]);return c}const MH=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=R("thenBranch",e,t,n),i=R("elseBranch",e,t,n),o=R("cond",e,t,n),a=R("args",e,t,n),c=await o.data();return c[0]?n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap):n.functionMap[i].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=R("body",e,t,n),i=R("cond",e,t,n),o=R("args",e,t,n),a=await n.functionMap[i].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap),c=o.map(m=>m.id);let u=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let p=o;for(;u[0];){const m=p;p=await n.functionMap[s].executeFunctionAsync(p,n.tensorArrayMap,n.tensorListMap);const y=p.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(p,n.tensorArrayMap,n.tensorListMap);u=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return p}case"LoopCond":{const s=R("pred",e,t,n);return[hr(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=hr(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Qn(i,t,n)!==void 0);if(s){const i=Qn(s,t,n);return[hr(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[hr(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[hr(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[hr(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),u=R("identicalElementShapes",e,t,n),p=R("name",e,t,n),m=new _H(p,i,s,o,u,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const 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s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=$H(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListGather":{const s=R("tensorListId",e,t,n),i=R("indices",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.gather(i,a,o)]}case"TensorListStack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=R("numElements",e,t,n),c=n.getTensorList(s.id);return[c.stack(i,o,a)]}case"TensorListFromTensor":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=WH(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListConcat":{const s=R("tensorListId",e,t,n),i=n.getTensorList(s.id),o=R("dtype",e,t,n),a=R("elementShape",e,t,n);return[i.concat(o,a)]}case"TensorListPushBack":{const s=R("tensorListId",e,t,n),i=R("tensor",e,t,n),o=n.getTensorList(s.id);return o.pushBack(i),[o.idTensor]}case"TensorListPopBack":{const 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qH=(e,t,n)=>{switch(e.op){case"Equal":return[ni(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[Pr(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[Ts(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[tr(R("a",e,t,n),R("b",e,t,n))];case"Less":return[dh(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[Mr(R("a",e,t,n),R("b",e,t,n))];case"LogicalAnd":return[Bs(R("a",e,t,n),R("b",e,t,n))];case"LogicalNot":return[ph(R("a",e,t,n))];case"LogicalOr":return[ep(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[$n(R("condition",e,t,n),R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qee="logical";const jH=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[at(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Pe(R("x",e,t,n),R("perm",e,t,n))];case"_FusedMatMul":const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=R("numArgs",e,t,n);if(o){if(a&&c!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&c!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[u,p]=R("args",e,t,n);return[bp({a:R("a",e,t,n),b:R("b",e,t,n),transposeA:R("transposeA",e,t,n),transposeB:R("transposeB",e,t,n),bias:u,activation:i,preluActivationWeights:p})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jee="matrices";const KH=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ro(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"FusedBatchNormV3":return[Ro(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"LRN":return[Tb(R("x",e,t,n),R("radius",e,t,n),R("bias",e,t,n),R("alpha",e,t,n),R("beta",e,t,n))];case"Softmax":return[Uo(R("x",e,t,n))];case"LogSoftmax":return[Qd(R("x",e,t,n))];case"SparseToDense":return[zb(R("sparseIndices",e,t,n),R("outputShape",e,t,n),R("sparseValues",e,t,n),R("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kee="normalization";const XH=(e,t,n)=>{switch(e.op){case"Max":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Xn(R("x",e,t,n),s,i)]}case"Mean":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[zt(R("x",e,t,n),s,i)]}case"Min":{const 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s=R("axis",e,t,n);return[zr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"PadV2":case"Pad":return[ki(R("x",e,t,n),R("padding",e,t,n),R("constantValue",e,t,n))];case"SpaceToBatchND":{const s=R("blockShape",e,t,n),i=R("paddings",e,t,n);return[fh(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[ah(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[bb(R("x",e,t,n),s,i)]}case"BroadcastTo":return[ch(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qee="transformation";function e0(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>DH(i,o,a));case"basic_math":return ee(()=>kH(i,o,a));case"control":return MH(i,o,a);case"convolution":return ee(()=>PH(i,o,a));case"creation":return ee(()=>zH(i,o,a));case"dynamic":return GH(i,o,a);case"evaluation":return ee(()=>VH(i,o,a));case"image":return ee(()=>YH(i,o,a));case"graph":return ee(()=>HH(i,o,a));case"logical":return ee(()=>qH(i,o,a));case"matrices":return ee(()=>jH(i,o,a));case"normalization":return ee(()=>KH(i,o,a));case"reduction":return ee(()=>XH(i,o,a));case"slice_join":return ee(()=>JH(i,o,a));case"spectral":return ee(()=>ZH(i,o,a));case"transformation":return ee(()=>QH(i,o,a));case"custom":const c=jN(i.op);if(c&&c.customExecutor)return c.customExecutor(new EH(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return s instanceof Promise?s.then(i=>[].concat(i)):[].concat(s)}class t0{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function n0(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const u=new Set,p=Object.keys(e).map(b=>ls(b)[0]);let m=[];s!=null&&(m=s.map(b=>ls(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((s0(b)||sY(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(p.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(u.has(w.name))return;u.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function eY(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>ls(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const u=new Set,p=[];for(;o.length>0;){const m=o.pop();u.add(m.name),t[m.name]||p.push(m),m.children.forEach(y=>{!u.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>u.has(b.name))&&o.push(y)})}return p}const tY=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],nY=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function s0(e){return tY.indexOf(e.op)>=0}function sY(e){return nY.indexOf(e.op)>=0}class eS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new eS(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=n0(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(u=>u.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return eY(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[ls(m)[0]]),i=t.map(m=>ls(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const u={},p={};return ee(()=>{const m=new t0(this.weightMap,u,p,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(I=>{const[T,v]=ls(I),N=[];N[v]=e[I],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let I=0;I<c.length;I++){const T=c[I];if(!y[T.name]){const v=e0(T,y,m);if(v instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=v,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(I=>Qn(I,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const u=JV(c.name,n,s);u!=null&&u.forEach(p=>{if(p&&!i.has(p.id)){const m=a[p.id];m===1?(p.dispose(),delete a[p.id]):m!=null&&a[p.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new t0(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Qn(y,a,o)),u=c.map(y=>y.id),p=Object.keys(e).map(y=>e[y].id),m=new Set([...u,...p,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[ls(E)[0]]),a=n.map(E=>ls(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:u,missingInputs:p,dynamicNode:m,syncInputs:y}=n0(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=ls(E),_=[];_[F]=e[E],w[D]=_});const I={},T=this.getFrozenTensorIds(w),v={};for(;b.length>0;){const E=this.processStack(o,b,t,w,v,T,a,I,u);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!s0(E)&&!Qn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${p}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,u){const p=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=lr(m.node.name,n)),e.indexOf(m.node)===-1){const b=e0(m.node,s,n);y||([y]=lr(m.node.name,n));const w=n.currentContext;b instanceof Promise?p.push(b.then(I=>(s[y]=I,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,u),I))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,u))}else this.processChildNodes(m.node,t,n,s,i,u)}return p}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=lr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(u=>!!Qn(u,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(u=>!!Qn(u,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=ls(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,u)=>o[u]===-1||o[u]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=ls(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=ls(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const iY="?tfjs-format=file",rY="model.json";class i0{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=kd(e,this.loadOptions);else{const t=By(e,this.loadOptions);if(t.length===0)t.push(kd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=Rd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new eS(KN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=KN.Instance.transformGraph(e.modelInitializer);this.initializer=new eS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=Uy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function oY(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${rY}${iY}`));const n=new i0(e,t);return await n.load(),n}const r0="2.6.0";function aY(e,t){return lm(e,t)}function lm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(lc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],u=lm(c,t,n,s);o[a]=u}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function cY(e,t=a0){return o0(e,t)}function o0(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(lc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(p=>p[a]),u=o0(c,t,n);o[a]=u}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function a0(e){return e===null?null:lc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function c0(e,t){const n=new Map;lm(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=lm(e,t,n);return s}function lc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function lY(e){return e==null||hY(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||Ln(e)}function hY(e){return e===null||typeof e!="object"&&typeof e!="function"}function uY(e){return aY(e,dY)}function dY(e){return e instanceof Q?{value:e.clone(),recurse:!1}:lc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class l0{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class tS extends l0{constructor(){super(tS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}tS.INITIAL_CAPACITY=32;function h0(e){return new mY(e)}function ete(e){let t=e;return nu(()=>({value:t++,done:!1}))}function nu(e){return new fY(e)}function u0(e,t){return new p0(e,t)}function tte(e,t,n){return u0(nu(e).take(t),n)}function pY(e,t=Qr.FAIL){return new TY(e,t)}class gn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new IY(this,e)}filter(e){return new LY(this,e)}map(e){return new SY(this,e)}mapAsync(e){return new d0(this,e)}serialMapAsync(e){return new d0(this,e).serial()}flatmap(e){return new xY(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new wY(this,e,t)}columnMajorBatch(e,t=!0,n=a0){const s=this.rowMajorBatch(e,t);return s.map(i=>cY(i,n))}concatenate(e,t){return new p0(h0([this,e]),t)}take(e){return e<0||e==null?this:new bY(this,e)}skip(e){return e<0||e==null?this:new yY(this,e)}prefetch(e){return new m0(this,e)}shuffle(e,t){return new AY(this,e,t)}serial(){return new gY(this)}}class mY extends gn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:uY(e),done:!1}}}class fY extends gn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class gY extends gn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class yY extends gn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;qe(e.value)}return this.upstream.next()}}class bY extends gn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class wY extends gn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class LY extends gn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;qe(e.value)}}}class SY extends gn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Zi(e.value),n=this.transform(e.value),s=Zi(n);for(const i of t)Nd(i,s)||i.dispose();return{value:n,done:!1}}}class IY extends gn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class d0 extends gn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Zi(e.value),n=await this.transform(e.value),s=Zi(n);for(const i of t)Nd(i,s)||i.dispose();return{value:n,done:!1}}}class nS extends gn{constructor(){super();this.outputQueue=new tS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class xY extends nS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Zi(e.value),n=this.transform(e.value),s=Zi(n);this.outputQueue.pushAll(n);for(const i of t)Nd(i,s)||i.dispose();return!0}}class p0 extends gn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Qr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Qr||(Qr={}));class TY extends gn{constructor(e,t=Qr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof gn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await c0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Qr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Qr.SHORTEST:return{value:null,done:!0};case Qr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class m0 extends gn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new l0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class AY extends m0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qa(n||qn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class hc{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),hs(async()=>(await n.iterator()).columnMajorBatch(e,t,CY),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,hs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,hs(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return hs(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return hs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return hs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,hs(async()=>{const s=nu(async()=>({value:await t.iterator(),done:!1}));return u0(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,hs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=qa(t||qn().toString());return hs(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,hs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}hc.MAX_BUFFER_SIZE=1e4;function hs(e,t=null){return new class extends hc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function vY(e){return hs(async()=>h0(e),e.length)}function NY(e){if(!lc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return hs(async()=>{const n=await c0(e,s=>{if(s instanceof hc)return{value:s.iterator(),recurse:!1};if(lc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return pY(n,Qr.SHORTEST)},t)}function CY(e){if(e===null)return null;const t=e[0];if(lY(t)){const n=RY(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function RY(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?as(e):en(e)}class f0 extends hc{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const hm='"',su=Symbol("out"),g0=Symbol("field"),um=Symbol("quote"),sS=Symbol("quoteafterquote"),y0=Symbol("quoteinquote");class b0 extends hc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new f0(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let u=null;if(c==="")if(a&&a.default!==void 0)u=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);u=void 0}else{const p=Number(c);if(isNaN(p))a&&a.dtype==="bool"?u=this.getBoolean(c):u=c;else if(!a||!a.dtype)u=p;else switch(a.dtype){case"float32":u=p;break;case"int32":u=Math.floor(p);break;case"bool":u=this.getBoolean(c);break;default:u=p}}a&&a.isLabel?s[o]=u:n[o]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=su;for(let a=0;a<i;a++)switch(o){case su:switch(e.charAt(a)){case hm:s=a+1,o=um;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=su;break;default:o=g0,s=a;break}break;case g0:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=su,s=a+1;break;default:}break;case um:switch(e.charAt(a)){case hm:o=sS;break;default:}break;case sS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=su,s=a+1;break;case hm:o=um;break;default:o=y0;break}break;case y0:switch(e.charAt(a)){case hm:o=um;break;default:}break;default:}if(o===sS?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class w0 extends gn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new w0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class L0 extends gn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=rs([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=Gr([o,i,c,a],[1,4])}else this.cropBox=Gr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new L0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=BT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=Vr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class S0{}class I0 extends gn{split(e){return new OY(this,e)}}class OY extends I0{constructor(e,t){super();this.upstream=e,this.impl=new EY(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class EY extends nS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class DY extends gn{decodeUTF8(){return new kY(this)}}class kY extends I0{constructor(e){super();this.upstream=e,this.impl=new FY(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class FY extends nS{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class x0 extends DY{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function _Y(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=WY(e));const i=await uT(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new x0(o,t)}else throw new Error(i.statusText)}const WY=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function T0(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class A0 extends S0{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(T0(this.input)&&C().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new x0(this.input,this.options)}}class v0 extends S0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return T0(this.url)?new A0(this.url,this.fileOptions).iterator():_Y(this.url,this.fileOptions)}}function $Y(e,t={}){return new b0(new v0(e),t)}function UY(e){const t=nu(e);return hs(async()=>t)}function BY(e){return hs(async()=>{const t=await e();return nu(()=>t.next())})}async function MY(e,t){return L0.create(e,t)}async function PY(e){return w0.create(e)}const N0="2.6.0";var zY=Object.freeze({__proto__:null,array:vY,Dataset:hc,zip:NY,CSVDataset:b0,TextLineDataset:f0,csv:$Y,func:UY,generator:BY,microphone:PY,webcam:MY,FileDataSource:A0,URLDataSource:v0,version_data:N0});function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const GY=wp,VY=rw,HY=ow,YY=aw,qY=dp;function iS(e,t,n,s){if(n==="linear")return e.linear(t);if(n==="relu")return e.relu(t);if(n==="elu")return Do(t);if(n==="relu6")return e.relu6(t);if(n==="prelu")return e.prelu(t,s);throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}class jY extends f{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new d(this,$s())}write(e,t,n){this.firstUse&&(this.firstUse=!1,C().get("IS_NODE")&&Qa(`
============================
Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,u=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,I=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=this.readSync(e.dataId),N=Qe(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let U=0;U<t.batchSize;++U){const Y=U*D,q=U*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let oe=0;oe<t.outDepth;++oe){const ce=oe*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,p+ce),pe=Y+oe*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let me=ye;for(;me<0;)me+=c;const Ie=Math.min(t.inHeight,m+ye),Se=pe+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-I;let Oe=We;for(;Oe<0;)Oe+=u;const $e=Math.min(t.inWidth,y+We),He=Se+Ee*B;let tt=T,bt=0,Jt=0;for(let 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ue=0;for(let he=0;he<b;he+=p){const pe=(J+he)/s;if(pe<0||pe>=n.outDepth||Math.floor(pe)!==pe)continue;for(let le=0;le<w;le+=m){const ye=(oe+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let me=0;me<I;me+=y){const Ie=(ce+me)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,pe,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,U,Y,q,B)}return E.toTensor()}maxPool3d(e,t){return Te(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Qe(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,u=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,I=t.padInfo.left,T=this.bufferSync(e);for(let v=0;v<t.batchSize;++v)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,p+D);for(let B=0;B<t.outHeight;++B){const U=B*i-w;let Y=U;for(;Y<0;)Y+=c;const q=Math.min(t.inHeight,m+U);for(let J=0;J<t.outWidth;++J){const oe=J*o-I;let ce=oe;for(;ce<0;)ce+=u;const ue=Math.min(t.inWidth,y+oe);let he=Number.NEGATIVE_INFINITY,pe=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let me=Y;me<q;me+=c){const Ie=me-U;for(let Se=ce;Se<ue;Se+=u){const Ee=Se-oe,We=T.get(v,le,me,Se,N);We>=he&&(he=We,pe=ye*m*y+Ie*m+Ee)}}}n.set(pe,v,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){Te([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,u=s.dilationDepth,p=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,I=y-1-s.padInfo.front,T=w-1-s.padInfo.left,v=b-1-s.padInfo.top,N=Qe(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let U=0;U<s.inHeight;++U)for(let Y=0;Y<s.inWidth;++Y){const q=B-I,J=U-v,oe=Y-T;let ce=0;for(let 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Xa(p,e.shape)}LRNGrad(e,t,n,s,i,o,a){Te(e,"LRNGrad");const c=e.shape[3],u=this.readSync(e.dataId),p=this.readSync(t.dataId),m=this.readSync(n.dataId),y=new Float32Array(e.size),b=e.size;for(let w=0;w<b;w++){const I=w%c,T=w-I+Math.max(0,I-s),v=w-I+Math.min(c,I+s+1);let N=0;for(let E=T;E<v;E++)N+=Math.pow(p[E],2);N=o*N+i;for(let E=T;E<v;E++){let D=-2*o*a*p[E]*m[w]/N;w===E&&(D+=Math.pow(N,-a)),D*=u[w],y[E]+=D}}return Xa(y,e.shape)}multinomial(e,t,n,s){Te(e,"multinomial");const i=t?e:Uo(e),o=i.shape[0],a=i.shape[1],c=ct([o,n],"int32"),u=this.readSync(c.dataId),p=this.readSync(i.dataId);for(let m=0;m<o;++m){const y=m*a,b=new Float32Array(a-1);b[0]=p[y];for(let T=1;T<b.length;++T)b[T]=b[T-1]+p[y+T];const w=qa(s.toString()),I=m*n;for(let T=0;T<n;++T){const v=w();u[I+T]=b.length;for(let N=0;N<b.length;N++)if(v<b[N]){u[I+T]=N;break}}}return c}oneHot(e,t,n,s){Te(e,"oneHot");const i=new Float32Array(e.size*t);i.fill(s);const o=this.readSync(e.dataId);for(let 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T=p[y*s+I];w+=T*c[I],b.push(T)}if(w<0||w>=e.size/a)throw new Error(`Invalid indices: ${b} does not index into ${e.shape}`);for(let I=0;I<a;I++)u.values[y*a+I]=m[w*a+I]}return u.toTensor().reshape(i)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=Ua(t,e,n),u=Ne(0),p=!0;return this.scatter(e,t,n,c,o,i,s,a,u,p)}fill(e,t,n){n=n||Oa(t);const s=So(n,we(e));return s.fill(t),$s().makeTensor(s,e,n,this)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)}zerosLike(e){const t=So(e.dtype,we(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,n){return sw(e,t,n)}scatter(e,t,n,s,i,o,a,c,u,p){const m=[s/i,i],y=this.readSync(e.dataId),b=this.readSync(t.dataId);if(s===0)return en([],n,t.dtype);const w=new kr(m,t.dtype);w.values.fill(this.readSync(u.dataId)[0]);for(let I=0;I<o;I++){const T=[];let v=0;for(let N=0;N<a;N++){const 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N=oS({inputs:{x:c},backend:n,attrs:{begin:[v,0],size:[1,o]}}),E=oS({inputs:{x:u},backend:n,attrs:{begin:[v,0],size:[1,o]}}),D=pi({inputs:{real:N,imag:E},backend:n}),{real:F,imag:_}=h4(D,t,n),B=ir(F,_);for(let U=0;U<o;U++){const Y=nw(B,U);y[v*o+U]=Y.real,b[v*o+U]=Y.imag}n.disposeIntermediateTensorInfo(N),n.disposeIntermediateTensorInfo(E),n.disposeIntermediateTensorInfo(D)}const w=n.makeTensorInfo(p,"float32",y),I=n.makeTensorInfo(p,"float32",b),T=pi({inputs:{real:w,imag:I},backend:n});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(I),T}function h4(e,t,n){const s=we(e.shape),i=n.data.get(e.dataId),o=n.data.get(i.complexTensorInfos.real.dataId).values,a=n.data.get(i.complexTensorInfos.imag.dataId).values;if(u4(s)){const c=hS(o,a,s,t,n),u=[e.shape[0],e.shape[1]];if(t){const 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${e}.`)});const{simpleAbsImpl:aK,addImpl:cK,ceilImpl:lK,expImpl:hK,expm1Impl:uK,floorImpl:dK,logImpl:pK,maxImpl:mK,multiplyImpl:fK,rsqrtImpl:gK,sliceImpl:yK,subImpl:bK,transposeImpl:Q0,uniqueImpl:wK}=Lq;class LK{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`float v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}}class SK{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`vec4 v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}}class IK{constructor(e,t,n){this.variableNames=["A"];const{windowSize:s,batchSize:i,outSize:o}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[i,o];const a=t==="max"?">":"<",c=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${c};
float candidate = getA(batch, inIdx);
if (candidate ${a} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}}function eC(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function us(e,t){return t===1?[e]:eC(e,t)}function xK(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}function Un(){let e,t,n,s,i,o,a,c,u,p;return C().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",i="texture",o="outputColor",a="out vec4 outputColor;",c=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,u="",p=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",i="texture2D",o="gl_FragColor",a="",c=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,p=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:u,defineRound:p}}function Xo(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function wm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function hte(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${wm(a)}, ${wm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${wm(o)}, ${wm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function yS(e){const t=Ot(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}const tC=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;const{getBroadcastDims:nC}=iw;function TK(e,t,n,s){const i=[];e.forEach(I=>{const T=we(I.shapeInfo.logicalShape);I.shapeInfo.isUniform?i.push(`uniform float ${I.name}${T>1?`[${T}]`:""};`):(i.push(`uniform sampler2D ${I.name};`),i.push(`uniform int offset${I.name};`))});const o=i.join(`
`),a=e.map(I=>AK(I,t,s)).join(`
`),c=t.texShape,u=Un(),p=CK(u);let m,y,b=EK(u);t.isPacked?(m=vK(t.logicalShape,c),y=OK(u)):(m=NK(t.logicalShape,c),y=RK(u)),s&&(b+=_K);const w=[b,p,y,o,m,a,n].join(`
`);return w}function bc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return qK(e);case 1:return KK(e);case 2:return JK(e);case 3:return QK(e);case 4:return t5(e);case 5:return n5(e);case 6:return s5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function sC(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return YK(e);case 1:return jK(e);case 2:return XK(e);case 3:return ZK(e);default:return e5(e)}}function AK(e,t,n=!1){let s="";n?s+=sC(e):s+=bc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=i5(e,t):s+=r5(e,t)),s}function vK(e,t){switch(e.length){case 0:return iC();case 1:return WK(e,t);case 2:return VK(e,t);case 3:return UK(e,t);default:return MK(e,t)}}function NK(e,t){switch(e.length){case 0:return iC();case 1:return $K(e,t);case 2:return HK(e,t);case 3:return BK(e,t);case 4:return PK(e,t);case 5:return zK(e,t);case 6:return GK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function CK(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function RK(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function OK(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function EK(e){const t=`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${DK}
${kK}
${FK}
`;return t}const DK=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,kK=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,FK=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,_K=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function iC(){return`
int getOutputCoords() {
return 0;
}
`}function WK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function $K(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function UK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),i=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${i};
index -= b * ${i};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec3(b, r, c);
}
`}function BK(e,t){const n=Xo(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function MK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),i=s*Math.ceil(e[e.length-2]/2);let o=i,a="",c="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],a=`
int b${u} = index / ${o};
index -= b${u} * ${o};
`+a,c=`b${u}, `+c;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${a}
int b = index / ${i};
index -= b * ${i};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec${e.length}(${c});
}
`}function PK(e,t){const n=Xo(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function zK(e,t){const n=Xo(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function GK(e,t){const n=Xo(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function VK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(ot(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;const s=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec2(r, c);
}
`}function HK(e,t){return ot(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Jo(e){return`offset${e}`}function YK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Un();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function qK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;const[s,i]=e.shapeInfo.texShape;if(s===1&&i===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;const[o,a]=e.shapeInfo.texShape,c=Jo(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${o}, ${a}, ${c});
return sampleTexture(${t}, uv);
}
`}function jK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=Un();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${o.texture2D}(${t}, uv);
}
`}function KK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${wc(e)}
}
`;const s=e.shapeInfo.texShape,i=s[0],o=s[1];if(o===1&&i===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;const a=Jo(t);return o===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${i}.0);
return sampleTexture(${t}, uv);
}
`:i===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${i}, ${o}, index + ${a});
return sampleTexture(${t}, uv);
}
`}function XK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=i[0],a=i[1],c=Un();if(i!=null&&ot(t,i))return`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${o}.0);
return ${c.texture2D}(${n}, uv);
}
`;const u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],p=Math.ceil(t[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col);
return ${c.texture2D}(${n}, uv);
}
`}function JK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape;if(i!=null&&ot(t,i)){const y=i[0],b=i[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
return sampleTexture(${n}, uv);
}
`}const{newShape:o,keptDims:a}=Rr(t),c=o;if(c.length<t.length){const y=Lc(e,c),b=["row","col"];return`
${bc(y)}
float ${s}(int row, int col) {
return ${s}(${Sc(b,a)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${wc(e)}
}
`;const u=i[0],p=i[1],m=Jo(n);return p===1?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${n}, uv);
}
`:u===1?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${p}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${m};
vec2 uv = uvFromFlat(${u}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function ZK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=Lc(e,y),I=["b","row","col"];return`
${sC(w)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Sc(I,b)});
}
`}const a=o[0],c=o[1],u=Math.ceil(t[2]/2),p=u*Math.ceil(t[1]/2),m=Un();return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${a}, ${c}, ${p}, ${u}, b, row, col);
return ${m.texture2D}(${n}, uv);
}
`}function QK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Rr(t),u=a;if(u.length<t.length){const I=Lc(e,u),T=["row","col","depth"];return`
${bc(I)}
float ${s}(int row, int col, int depth) {
return ${s}(${Sc(T,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${i}, ${o}, 1)));
${wc(e)}
}
`;const p=e.shapeInfo.texShape,m=p[0],y=p[1],b=e.shapeInfo.flatOffset;if(y===i&&b==null)return`
float ${s}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${y}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(y===o&&b==null)return`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;const w=Jo(n);return`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth + ${w};
vec2 uv = uvFromFlat(${m}, ${y}, index);
return sampleTexture(${n}, uv);
}
`}function e5(e){const t=e.shapeInfo.logicalShape,n=t.length,s=e.name,i="get"+s.charAt(0).toUpperCase()+s.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],c=a[0],u=a[1],p=Math.ceil(t[n-1]/2);let m=p*Math.ceil(t[n-2]/2),y="int b, int row, int col",b=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let I=2;I<n-1;I++)y=`int b${I}, `+y,m*=t[n-I-1],b=`b${I} * ${m} + `+b;const w=Un();return`
vec4 ${i}(${y}) {
int index = ${b};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
return ${w.texture2D}(${s}, uv);
}
`}function t5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:u}=Rr(t);if(c.length<t.length){const I=Lc(e,c),T=["row","col","depth","depth2"];return`
${bc(I)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Sc(T,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${o}, ${i}, 1)));
${wc(e)}
}
`;const p=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,y=m[0],b=m[1];if(b===a&&p==null)return`
float ${s}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${b}.0, ${y}.0);
return sampleTexture(${n}, uv);
}
`;if(b===i&&p==null)return`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${b}.0, ${y}.0);
return sampleTexture(${n}, uv);
}
`;const w=Jo(n);return`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} +
depth * ${i} + depth2;
vec2 uv = uvFromFlat(${y}, ${b}, index + ${w});
return sampleTexture(${n}, uv);
}
`}function n5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:u,keptDims:p}=Rr(t);if(u.length<t.length){const T=Lc(e,u),v=["row","col","depth","depth2","depth3"];return`
${bc(T)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${Sc(v,p)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${a}, ${o}, ${i})) +
depth3;
${wc(e)}
}
`;const m=e.shapeInfo.flatOffset,y=e.shapeInfo.texShape,b=y[0],w=y[1];if(w===c&&m==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${o}, ${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${w}.0, ${b}.0);
return sampleTexture(${n}, uv);
}
`;if(w===i&&m==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${w}.0, ${b}.0);
return sampleTexture(${n}, uv);
}
`;const I=Jo(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${a} + depth * ${o} +
depth2 * ${i} + depth3 + ${I};
vec2 uv = uvFromFlat(${b}, ${w}, index);
return sampleTexture(${n}, uv);
}
`}function s5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Rr(t);if(i.length<t.length){const v=Lc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
${bc(v)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${Sc(N,o)});
}
`}const a=t[5],c=t[4]*a,u=t[3]*c,p=t[2]*u,m=t[1]*p;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${m}, ${p}, ${u}, ${c})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${wc(e)}
}
`;const y=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],I=b[1];if(I===m&&y==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${p}, ${u}, ${c}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${I}.0, ${w}.0);
return sampleTexture(${n}, uv);
}
`;if(I===a&&y==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${I}.0, ${w}.0);
return sampleTexture(${n}, uv);
}
`;const T=Jo(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${m} + col * ${p} + depth * ${u} +
depth2 * ${c} + depth3 * ${a} + depth4 + ${T};
vec2 uv = uvFromFlat(${w}, ${I}, index);
return sampleTexture(${n}, uv);
}
`}function wc(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function i5(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,c=nC(e.shapeInfo.logicalShape,t.logicalShape),u=Et(a),p=a-o;let m;const y=["x","y","z","w","u","v"];o===0?m="":a<2&&c.length>=1?m="coords = 0;":m=c.map(E=>`coords.${y[E+p]} = 0;`).join(`
`);let b="";a<2&&o>0?b="coords":b=e.shapeInfo.logicalShape.map((E,D)=>`coords.${y[D+p]}`).join(", ");let w="return outputValue;";const I=we(e.shapeInfo.logicalShape),T=I===1,v=we(t.logicalShape),N=v===1;if(o===1&&!T&&!N)w=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(T&&!N)a===1?w=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:w=`
return vec4(outputValue.x);
`;else if(c.length){const E=o-2,D=o-1;c.indexOf(E)>-1&&c.indexOf(D)>-1?w="return vec4(outputValue.x);":c.indexOf(E)>-1?w="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":c.indexOf(D)>-1&&(w="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${i}() {
${u} coords = getOutputCoords();
${m}
vec4 outputValue = get${s}(${b});
${w}
}
`}function r5(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=t.texShape,a=e.shapeInfo.texShape,c=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&c===u&&e.shapeInfo.flatOffset==null&&ot(a,o))return`
float ${i}() {
return sampleTexture(${n}, resultUV);
}
`;const p=Et(u),m=nC(e.shapeInfo.logicalShape,t.logicalShape),y=u-c;let b;const w=["x","y","z","w","u","v"];c===0?b="":u<2&&m.length>=1?b="coords = 0;":b=m.map(T=>`coords.${w[T+y]} = 0;`).join(`
`);let I="";return u<2&&c>0?I="coords":I=e.shapeInfo.logicalShape.map((T,v)=>`coords.${w[v+y]}`).join(", "),`
float ${i}() {
${p} coords = getOutputCoords();
${b}
return get${s}(${I});
}
`}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Lc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Sc(e,t){return t.map(n=>e[n]).join(", ")}class o5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,u=Et(c),p=us("coords",c);let m,y;if(o===1){y=c+1;const U=Et(y);m=`
${U} sourceLocR = ${U}(${p.join()}, 0);
++${p[c-1]};
${U} sourceLocG = ${U}(${p.join()}, 0);
++${p[c-2]};
${U} sourceLocA = ${U}(${p.join()}, 0);
--${p[c-1]};
${U} sourceLocB = ${U}(${p.join()}, 0);
--${p[c-2]};`}else y=c,m=`
${u} sourceLocR = coords;
++${p[c-1]};
${u} sourceLocG = coords;
++${p[c-2]};
${u} sourceLocA = coords;
--${p[c-1]};
${u} sourceLocB = coords;
--${p[c-2]};`;const b=["x","y","z","w","u","v"].slice(0,y),w="."+b[y-1],I=b.map(U=>"int "+U),T=us("sourceLocR",y-1).concat("inIdx.r"),v=us("sourceLocG",y-1).concat("inIdx.g"),N=us("sourceLocB",y-1).concat("inIdx.b"),E=us("sourceLocA",y-1).concat("inIdx.a"),D=n==="max"?"greaterThan":"lessThan",F=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${T.join()}),
getBestIndicesAChannel(${v.join()}),
getBestIndicesAChannel(${N.join()}),
getBestIndicesAChannel(${E.join()})));`,_=`vec4(
getAChannel(${T.join()}),
hasNextCol ? getAChannel(${v.join()}) : 0.,
hasNextRow ? getAChannel(${N.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${E.join()}) : 0.)`,B=s?"":`
float getBestIndicesAChannel(${I.join()}) {
return getChannel(getBestIndicesA(${b.join()}),
vec2(${b.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${I.join()}) {
return getChannel(getA(${b.join()}),
vec2(${b.slice(-2).join()}));
}
${B}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${p[c-1]} < ${a[c-1]-1};
bool hasNextRow = ${p[c-2]} < ${a[c-2]-1};
${m}
ivec4 srcIdx = ivec4(sourceLocR${w}, sourceLocG${w},
sourceLocB${w}, sourceLocA${w}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${_};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${F}
vec4 candidate = ${_};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${D}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}}class a5{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=e.dilationHeight,a=e.dilationWidth,c=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=c-1-e.padInfo.top,m=u-1-e.padInfo.left,y=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${p}, ${m});
const float avgMultiplier = float(${y});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${c};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${a}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}}class c5{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,m=e.effectiveFilterDepth,y=e.effectiveFilterHeight,b=e.effectiveFilterWidth,w=m-1-e.padInfo.front,I=y-1-e.padInfo.top,T=b-1-e.padInfo.left,v=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${w}, ${I}, ${T});
const float avgMultiplier = float(${v});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${m};
wD += ${c}) {
float dyD = float(dyDCorner + wD) / ${i}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${y};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${b};
wC += ${p}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}}const rC={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class oC{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=nt(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}}const aC=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,bS="return a + b;",wS="return a - b;",cC="return a * b;",l5=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,h5=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,ute="return (a - b) * (a - b);",u5="return float(a == b);",d5="return float(a != b);",p5="return float(a < b);",m5="return float(a <= b);",f5="return float(a > b);",g5="return float(a >= b);",y5="return float(a >= 1.0 && b >= 1.0);",b5="return float(a >= 1.0 || b >= 1.0);",w5=aC+`
return max(a, b);
`,L5=aC+`
return min(a, b);
`,S5=`if (b == 0.0) return NAN;
return mod(a, b);`,I5="return (b >= 1.0) ? a : a * (b + 1.0);",lC="return (a < 0.) ? b * a : a;";class un{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=nt(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}}const Lm=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,x5=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,T5=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+Lm+`
return result;
`,hC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,A5=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,v5=`
return vec4(equal(a, b));
`,N5=`
return vec4(notEqual(a, b));
`,C5=`
return vec4(lessThan(a, b));
`,R5=`
return vec4(lessThanEqual(a, b));
`,O5=`
return vec4(greaterThan(a, b));
`,E5=`
return vec4(greaterThanEqual(a, b));
`,D5=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,k5=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,F5=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Lm+`
return result;
`,_5=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Lm+`
return result;
`,W5=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Lm+`
return result;
`;class to{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=nt(t,n);const i=this.outputShape.length;let o="";if(s)if(i===0||we(this.outputShape)===1)o=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else{const a=Et(i);if(o=`
${a} coords = getOutputCoords();
`,i===1)o+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{const c=us("coords",i);o+=`
bool nextRowOutOfBounds =
(${c[i-2]} + 1) >= ${this.outputShape[i-2]};
bool nextColOutOfBounds =
(${c[i-1]} + 1) >= ${this.outputShape[i-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${o}
setOutput(result);
}
`}}class $5{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,s)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class U5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,s)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class B5{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}}class M5{constructor(e){this.outputShape=[],this.outputShape=Ur(e,1),this.variableNames=e.map((o,a)=>`T${a}`);const t=new Array(e.length-1);t[0]=e[0][1];for(let o=1;o<t.length;o++)t[o]=t[o-1]+e[o][1];const n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let o=1;o<t.length;o++){const a=t[o-1];n.push(`else if (yC < ${t[o]}) setOutput(getT${o}(yR, yC-${a}));`)}const s=t.length,i=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${i}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}}class P5{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Ur(e,t);const n=this.outputShape,s=n.length,i=Et(s),o=us("coords",s),a=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((I,T)=>`T${T}`);const c=new Array(e.length-1);c[0]=e[0][t];for(let I=1;I<c.length;I++)c[I]=c[I-1]+e[I][t];const u=a[t],p=a.slice(-2),m=a.join();let y=`if (${u} < ${c[0]}) {
return getChannel(
getT0(${m}), vec2(${p.join()}));
}`;for(let I=1;I<c.length;I++){const T=c[I-1];y+=`
if (${u} < ${c[I]} && ${u} >= ${c[I-1]}) {
return getChannel(
getT${I}(${Sm(a,u,T)}),
vec2(${Sm(p,u,T)}));
}`}const b=c.length,w=c[c.length-1];y+=`
return getChannel(
getT${b}(${Sm(a,u,w)}),
vec2(${Sm(p,u,w)}));`,this.userCode=`
float getValue(${a.map(I=>"int "+I)}) {
${y}
}
void main() {
${i} coords = getOutputCoords();
vec4 result = vec4(getValue(${o}), 0., 0., 0.);
${o[s-1]} = ${o[s-1]} + 1;
if (${o[s-1]} < ${n[s-1]}) {
result.g = getValue(${o});
}
${o[s-2]} = ${o[s-2]} + 1;
if (${o[s-2]} < ${n[s-2]}) {
result.a = getValue(${o});
}
${o[s-1]} = ${o[s-1]} - 1;
if (${o[s-2]} < ${n[s-2]} &&
${o[s-1]} < ${n[s-1]}) {
result.b = getValue(${o});
}
setOutput(result);
}
`}}function Sm(e,t,n){const s=e.indexOf(t),i=e.map((o,a)=>a===s?`${o} - ${n}`:o);return i.join()}class z5{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,i=e.padInfo.left,o=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${o}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class G5{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=e.dataFormat==="channelsLast",a=t-1-e.padInfo.top,c=n-1-e.padInfo.left,u=o?1:2,p=o?2:3,m=o?3:1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${m}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${p}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${o}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}}class V5{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,i=e.padInfo.front,o=e.padInfo.top,a=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class H5{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=t-1-e.padInfo.front,u=n-1-e.padInfo.top,p=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${c}, ${u}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${i}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}}class Y5{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,i=e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${o} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}}class q5{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.top,a=n-1-e.padInfo.left,c=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${o}, ${a});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${c}; dm++) {
int d2 = d1 * ${c} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}}class uC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,c=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,y=e.filterWidth,b=Math.floor(e.inChannels/4)*4,w=e.inChannels%4,I=e.dataFormat==="channelsLast",T=I?1:2,v=I?2:3,N=I?3:1;let E="",D="";n&&(s?E=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:E=`
float activation(float x) {
${n}
}
`,D="result = activation(result);");const F=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${E}
const ivec2 strides = ivec2(${a}, ${c});
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${N}];
ivec2 xRCCorner =
ivec2(coords[${T}], coords[${v}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${y}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${b}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${I}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${w===1}) {
if (${I}) {
dotProd +=
getX(batch, xR, xC, ${b}) *
getW(wR, wC, ${b}, d2);
} else {
dotProd +=
getX(batch, ${b}, xR, xC) *
getW(wR, wC, ${b}, d2);
}
} else if (${w===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${b}, d2),
getW(wR, wC, ${b} + 1, d2)
);
if (${I}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${b}),
getX(batch, xR, xC, ${b} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${b}, xR, xC),
getX(batch, ${b} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${w===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${b}, d2),
getW(wR, wC, ${b} + 1, d2),
getW(wR, wC, ${b} + 2, d2)
);
if (${I}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${b}),
getX(batch, xR, xC, ${b} + 1),
getX(batch, xR, xC, ${b} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${b}, xR, xC),
getX(batch, ${b} + 1, xR, xC),
getX(batch, ${b} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${F}
${D}
setOutput(result);
}
`}}class j5{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,m=e.filterDepth,y=e.filterHeight,b=e.filterWidth,w=Math.floor(e.inChannels/4)*4,I=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${i}, ${o}, ${a});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${m}; wF++) {
int xF = xFCorner + wF * ${c};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${y}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${w}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${I===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${w}) *
getW(wF, wR, wC, ${w}, d2);
} else if (${I===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${w}),
getX(batch, xF, xR, xC, ${w} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${w}, d2),
getW(wF, wR, wC, ${w} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${I===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${w}),
getX(batch, xF, xR, xC, ${w} + 1),
getX(batch, xF, xR, xC, ${w} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${w}, d2),
getW(wF, wR, wC, ${w} + 1, d2),
getW(wF, wR, wC, ${w} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}}class dC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,y=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,I=e.outChannels/e.inChannels;let T="",v="";n&&(s?T=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:T=`
float activation(float x) {
${n}
}
`,v="result = activation(result);");const N=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${T}
const ivec2 strides = ivec2(${u}, ${p});
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${I};
int q = d2 - d1 * ${I};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${b}; wR++) {
int xR = xRCorner + wR * ${m};
if (xR < 0 || xR >= ${i}) {
continue;
}
for (int wC = 0; wC < ${w}; wC++) {
int xC = xCCorner + wC * ${y};
if (xC < 0 || xC >= ${o}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${N}
${v}
setOutput(result);
}
`}}class pC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,y=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,I=w;let T="int xR; int xC; int xCOffset;";for(let D=0;D<b;D++)for(let F=0;F<w;F++)T+=`
vec4 xTexelR${D}C${F*2} = vec4(0.);
vec4 wR${D}C${F} = vec4(0.);
vec4 xR${D}C${F} = vec4(0.);`;for(let D=0;D<b;D++)for(let F=0;F<I;F++){const _=F*2;if(T+=`
xR = xRCorner + ${D*m};
xC = xCCorner + ${_*y};
`,p===1){if(_<w&&(c%2===1?T+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${o}) {
xTexelR${D}C${_}.zw = vec2(0.);
}
} else {
xTexelR${D}C${_} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${o}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${o}) {
previous.zw = vec2(0.);
}
xR${D}C${_} = vec4(previous.zw, xTexelR${D}C${_}.xy);
} else {
xR${D}C${_} = vec4(0, 0, xTexelR${D}C${_}.xy);
}
`:T+=`
if(xR >= 0 && xR < ${i} && xC >= 0 && xC < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xC, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
xR${D}C${_} = xTexelR${D}C${_};
`,_+1<w)){const B=c%2===0?Sy(y):y;y%2===0&&c%2===1||y%2!==0&&c%2!==1?(T+=`
xCOffset = xC + ${c%2} + ${B};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xCOffset, d1);
}
`,y>1&&(T+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
`),T+=`
xR${D}C${_+1} = vec4(
xTexelR${D}C${_}.zw, xTexelR${D}C${_+2}.xy);
`):T+=`
xCOffset = xC + ${B};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xCOffset, d1);
}
xR${D}C${_+1} = xTexelR${D}C${_+2};
`}}else _<w&&(T+=`
if(xR >= 0 && xR < ${i}) {
`,c%2===1?(T+=`
xCOffset = xC + 1 - ${p};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${D}C${_+2} = vec4(0.);
}
xR${D}C${_} = vec4(
xTexelR${D}C${_}.zw, xTexelR${D}C${_+2}.zw);
`,_+1<w&&(T+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${p};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${D}C${_+1} = vec4(xTexelR${D}C${_+2}.xy, final.xy);
`)):(T+=`
if(xC >= 0 && xC < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xC, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
xCOffset = xC + ${p};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${D}C${_+2} = vec4(0.);
}
xR${D}C${_} = vec4(
xTexelR${D}C${_}.xy, xTexelR${D}C${_+2}.xy);
`,_+1<w&&(T+=`
xR${D}C${_+1} = vec4(
xTexelR${D}C${_}.zw, xTexelR${D}C${_+2}.zw);
`)),T+="}");_<w&&(T+=`
vec4 wTexelR${D}C${_} = getW(${D}, ${_}, d1, q);
wR${D}C${_} = vec4(wTexelR${D}C${_}.xz, wTexelR${D}C${_}.xz);
`,_+1<w&&(T+=`
vec4 wTexelR${D}C${_+1} = getW(${D}, ${_+1}, d1, q);
wR${D}C${_+1} =
vec4(wTexelR${D}C${_+1}.xz, wTexelR${D}C${_+1}.xz);`))}for(let D=0;D<b;D++)for(let F=0;F<w;F++)T+=`dotProd += xR${D}C${F} * wR${D}C${F};`;let v="",N="";n&&(s?v=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:v=`vec4 activation(vec4 x) {
${n}
}`,N="result = activation(result);");const E=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${v}
const ivec2 strides = ivec2(${u}, ${p});
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${T}
vec4 result = dotProd;
${E}
${N}
setOutput(result);
}
`}}class K5{constructor(e,t,n,s,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[o,a,c,u]=e,[p]=t,[m,y]=n;this.outputShape=[p,m,y,u];const b=s==="bilinear"?1:0,[w,I]=[`${a-1}.0`,`${c-1}.0`],[T,v,N]=m>1?[`${(a-1)/(m-1)}`,"(y2-y1) * height_ratio",`y1*${w} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${w}`],[E,D,F]=y>1?[`${(c-1)/(y-1)}`,"(x2-x1) * width_ratio",`x1*${I} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${I}`];this.userCode=`
const float height_ratio = float(${T});
const float width_ratio = float(${E});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${o}) {
return;
}
float height_scale = ${v};
float width_scale = ${D};
float in_y = ${N};
if( in_y < 0.0 || in_y > ${w} ) {
setOutput(float(${i}));
return;
}
float in_x = ${F};
if( in_x < 0.0 || in_x > ${I} ) {
setOutput(float(${i}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${b} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}}class mC{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const s=e.length,i=t?"0.0":`getX(${fC(s,"coords")})`,o=e[e.length-1];let a="",c="";t?(a=n?`end != ${o-1}`:"end != 0",c=n?"end + 1":"end - 1"):(a=n?`end + pow2 < ${o}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${Et(s)} coords = getOutputCoords();
int end = ${gC(s,"coords")};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${a}) {
int idx = ${c};
${gC(s,"coords")} = idx;
val += getX(${fC(s,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function fC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function gC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class X5{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=au.DENSE;const t=lu(e),n=Un();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Xo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${n.output} = result;
}
`}}class J5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=au.DENSE;const t=lu(e),n=Un();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Xo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${n.output} = result;
}
`}}class Z5{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}class Q5{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}}class e8{constructor(e){this.variableNames=["A"],this.outTexUsage=Cs.DOWNLOAD;const t=Un();this.outputShape=e,this.userCode=`
${tC}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}}class t8{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Cs.DOWNLOAD;const t=Un();this.outputShape=e,this.userCode=`
${tC}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}}class n8{constructor(e,t,n=!1){this.variableNames=["A"];const s=Un(),[i,o]=t;this.outputShape=e;let a="result";n&&(a="floor(result * 255. + 0.5)"),this.userCode=`
${yS(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${o};
int c = imod(flatIndex, ${o});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${o}.0, ${i}.0);
vec4 values = ${s.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${s.output} = vec4(${a}, 0., 0., 0.);
}
`}}class s8{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const s=Un(),[i,o]=t;this.outputShape=e;let a="",c="result";n&&(c="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let p=0;p<=1;p++){const m=u*2+p;a+=`
localCoords = coords;
if(localCoords[2] + ${p} < ${e[2]}) {
localCoords[2] += ${p};
if(localCoords[1] + ${u} < ${e[1]}) {
localCoords[1] += ${u};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${o};
c = imod(flatIndex, ${o});
uv = (vec2(c, r) + halfCR) / vec2(${o}.0, ${i}.0);
values = ${s.texture2D}(A, uv);
if(offset == 0) {
result[${m}] = values[0];
} else if(offset == 1) {
result[${m}] = values[1];
} else if(offset == 2) {
result[${m}] = values[2];
} else {
result[${m}] = values[3];
}
}
}
`}this.userCode=`
${yS(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${a}
${s.output} = ${c};
}
`}}const yC={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"};class bC{constructor(e,t,n){this.variableNames=["real","imag"];const s=t[1];this.outputShape=t;const i=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,o=n?`${s}.0`:"1.0";this.userCode=`
const float exponentMultiplier = ${i};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${e}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${o};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}}class i8{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}class r8{constructor(e,t,n){this.variableNames=["A","indices"];const s=e.slice();s[n]=t,this.outputShape=s,this.rank=s.length;const i=Et(this.rank),o=o8(e,n);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}}function o8(e,t){const n=e.length;if(n>4)throw Error(`Gather for rank ${n} is not yet supported`);if(n===1)return"int(getIndices(resRC))";const s=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[];for(let o=0;o<e.length;o++)o===t?i.push(`int(getIndices(${s[o]}))`):i.push(`${s[o]}`);return i.join()}class a8{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;const s=Et(t.length),i=Et(n.length),o=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${this.strides});
void main() {
${i} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${o};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}}function c8(e){const t=Un(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return $j(e,n)}function l8(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Gj(e,t)}function h8(e){const t=new Uint16Array([0,1,2,2,1,3]);return Vj(e,t)}function uu(e,t,n,s,i,o){Yj(t,n);const a=Hj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function wC(e){return e.internalFormatFloat}function u8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,wC(s),s.textureFormatFloat,e.FLOAT)}function LC(e){return e.internalFormatHalfFloat}function d8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,LC(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function SC(e){return e.downloadTextureFormat}function p8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,SC(s),e.RGBA,e.UNSIGNED_BYTE)}function IC(e){return e.internalFormatPackedFloat}function m8(e,t,n,s){const[i,o]=fc(t,n);return uu(e,i,o,IC(s),e.RGBA,e.FLOAT)}function xC(e){return e.internalFormatPackedHalfFloat}function f8(e,t,n,s){const[i,o]=fc(t,n);return uu(e,i,o,xC(s),e.RGBA,s.textureTypeHalfFloat)}function g8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=K0(e,t,"clipSpacePos",n,3,o,s);return a&&K0(e,t,"uv",n,2,o,i)}function y8(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,u;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,u=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,u=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function b8(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function w8(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function L8(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function S8(e,t,n,s){const[i,o]=cu(t,n),a=4,c=new Uint8Array(Oj(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function I8(e,t,n,s,i,o,a,c){const u=e,p=new Float32Array(Ej(o,a));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,p),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),p}function x8(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class T8{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Nj(t,e)):this.gl=Mi(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=pm(this.gl,i),Vs(this.gl,o))this.textureHalfFloatExtension=pm(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Vs(this.gl,s))this.colorBufferHalfFloatExtension=pm(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Vs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=l8(this.gl),this.indexBuffer=h8(this.gl),this.framebuffer=qj(this.gl),this.textureConfig=dS(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),u8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),d8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),p8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),b8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),y8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),f8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),m8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(X0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>S8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return I8(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return L8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=w8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>x8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=Uj(t,e),s=c8(t),i=Pj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),zj(t,i),this.debug&&pS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=g8(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pS(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Kj(this.gl,e,t):Xj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Jj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=fc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&pS(this.gl,this.program),mm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=pm(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await Iy(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=A8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;Iy(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mS(this.gl,e,this.framebuffer),this.debug&&mm(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mS(this.gl,this.outputTexture,this.framebuffer),this.debug&&mm(this.gl)):X0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;mS(s,e,this.framebuffer),this.debug&&mm(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function A8(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function v8(e,t,n,s){const i=t.userCode,o=n.map((w,I)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[I],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},u=TK(o,c,i,t.packedInputs),p=e.createProgram(u);let m=null;const y=e.getUniformLocation(p,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(p,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const I=t.variableNames[w],T=!1;b[I]=e.getUniformLocation(p,I,T),b[`offset${I}`]=e.getUniformLocation(p,`offset${I}`,T)}return{program:t,source:u,webGLProgram:p,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function TC(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,u=o.isUniform?null:o.texData.texShape;if(!ot(c,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${u} must match`)})}function N8(e,t,n,s,i){TC(t.inShapeInfos,n),TC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,u)=>{const p=t.program.variableNames[u],m=t.uniformLocations[p],y=t.uniformLocations[`offset${p}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,u)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function C8(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,u=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${u}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class R8{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:u,dilationWidth:p,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,I=i*s,T=Un(),v=y==="channelsLast",N=v?0:1,E=v?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
blockIndex = rc.y + ${_};
pos = rc.x + ${F};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${u})) * ${a} - ${w};
d0 = offsetY + ${m} * (pos / ${I});
if(d0 < ${t[N]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${u}.) * ${o}. - ${b}.);
d1 = offsetX + ${p} * (int(mod(float(pos), ${I}.) / ${i}.));
if(d1 < ${t[E]} && d1 >= 0) {
ch = int(mod(float(pos), ${i}.));
if (${v}) {
innerDims = vec2(d1, ch);
result[${F*2+_}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${F*2+_}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${D}
${T.output} = result;
}
`}}class O8{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[];const o=t,a=e[3]-1;this.outputShape=e;let c;const u=`float(${n}) + float(${s}) * sum`;i===.5?c=`inversesqrt(${u})`:i===1?c=`1.0/(${u})`:c=`exp(log(${u}) * float(-${i}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${o}; j <= ${o}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${a}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${c};
setOutput(val);
}
`}}class E8{constructor(e,t,n,s,i){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=i,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${i})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${i});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}}class D8{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const o=t,a=e[3]-1;this.outputShape=e;let c;const u=`float(${n}) + float(${s}) * sum`;i===.5?c=`inversesqrt(${u})`:i===1?c=`1.0/(${u})`:c=`exp(log(${u}) * float(-${i}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${o};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${o}; j <= ${o}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${c};
setOutput(result);
}
`}}class k8{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,i=e.effectiveFilterHeight,o=e.effectiveFilterWidth,a=i-1-e.padInfo.top,c=o-1-e.padInfo.left,u=i*o-1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${i};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${o} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}}class F8{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,i=e.dilationDepth,o=e.dilationHeight,a=e.dilationWidth,c=e.effectiveFilterDepth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,m=c-1-e.padInfo.front,y=u-1-e.padInfo.top,b=p-1-e.padInfo.left,w=c*u*p-1;this.userCode=`
const ivec3 pads = ivec3(${m}, ${y}, ${b});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${c};
wD += ${i}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${p};
wC += ${a}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${w} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${p} +
wR * ${p} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}}class LS{constructor(e,t,n=!1,s=!1,i=!1,o=null,a=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;const c=n?e[1]:e[2],u=Math.ceil(c/2),p=n?"i * 2, rc.y":"rc.y, i * 2",m=s?"rc.z, i * 2":"i * 2, rc.z",y=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],b=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let w="",I="";o&&(a?w=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:w=`vec4 activation(vec4 x) {
${o}
}`,I="result = activation(result);");const T=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${w}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
vec4 a = getMatrixA(rc.x, ${p});
vec4 b = getMatrixB(rc.x, ${m});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${y[0]} * ${b[0]});
result += (${y[1]} * ${b[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${T}
${I}
setOutput(result);
}
`}}class _8{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}class W8{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}}class $8{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;const t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{const n=us("rc",t),s=Et(t),i=B8(t,e,n),o=M8(t,e[e.length-1],e[e.length-2],n),a=P8(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${i}) {
setOutput(vec4(0));
} else {
${o}
setOutput(vec4(${a}));
}
}
`}}}function U8(e,t){const n=[];for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=`${s===0?"r":"rp1"}, ${i===0?"c":"cp1"}`;for(let a=2;a<e;a++)o=`${t[t.length-1-a]},`+o;n.push(o)}return n}function B8(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let i=e-2;i<e;i++)s+=`${n[i]} >= ${t[i]}`,i<e-1&&(s+="||");return s}function M8(e,t,n,s){if(e===1)return"";const i=s.slice(-2);return`
int r = ${i[0]};
int c = ${i[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function P8(e,t){const n=e.length,s=U8(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${s[0]}),
cEdge ? 0. : getA(${s[1]}),
rEdge ? 0. : getA(${s[2]}),
rEdge || cEdge ? 0. : getA(${s[3]})`}class z8{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);const s=e.length,i=Et(s),o=t.map(u=>u[0]).join(","),a=t.map((u,p)=>u[0]+e[p]).join(","),c=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${o};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${i} start = ${i}(${o});
${i} end = ${i}(${a});
void main() {
${i} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${i} coords = outC - start;
setOutput(getX(${c}));
}
}
`}}class G8{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((I,T)=>I[0]+e[T]+I[1]);const s=e.length,i=Et(s),o=t.map(I=>I[0]).join(","),a=t.map((I,T)=>I[0]+e[T]).join(","),c=us("rc",s),u=us("source",s),p=`${c[s-1]} < ${this.outputShape[s-1]}`,m=s===1?"source":`vec2(${u.slice(-2).join()})`,y=[`${i} rc = outputLoc;`,`${c[s-1]} += 1;
if(${p}) {
`,s===1?"":`}
rc = outputLoc;
${c[s-2]} += 1;
if(${c[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${c[s-1]} += 1;
if(${p}) {`],b=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let w="";for(let I=0,T=s===1?2:4;I<T;I++)w+=`
${y[I]}
if (${b}) {
result[${I}] = float(${n});
} else {
${i} source = rc - start;
result[${I}] = getChannel(getX(${u.join()}), ${m});
}
`;w+=s===1?"} ":"}}",this.userCode=`
const ${i} start = ${i}(${o});
const ${i} end = ${i}(${a});
void main() {
${i} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${w}
setOutput(result);
}
`}}class du{constructor(e,t,n,s=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");const o=e.filterWidth,a=e.strideHeight,c=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,m=e.effectiveFilterHeight,y=e.effectiveFilterWidth,b=e.padInfo.top,w=e.padInfo.left;this.outputShape=e.outShape;const I=t==="avg",T=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,v=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let N="0.0";if(I||(N="-1.0 / 1e-20"),n){const U=">=";this.userCode=`
const ivec2 strides = ivec2(${a}, ${c});
const ivec2 pads = ivec2(${b}, ${w});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${m};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${y};
wC += ${p}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${U} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?i?T:v:`wR * ${y} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const E="max";let D=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(D="avgValue / count");const F=Math.floor(o/4)*4,_=o%4,B=`
if (${I}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${E}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${a}, ${c});
const ivec2 pads = ivec2(${b}, ${w});
const float initializationValue = ${N};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${N});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${m};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${F}; wC += 4) {
int xC = xCCorner + wC * ${p};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${p}, d),
getValue(batch, xR, xC + 2 * ${p}, d),
getValue(batch, xR, xC + 3 * ${p}, d)
);
${B}
}
int xC = xCCorner + ${F};
if (${_===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${B}
} else if (${_===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${p}, d),
initializationValue,
initializationValue
);
${B}
} else if (${_===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${p}, d),
getValue(batch, xR, xC + 2 * ${p}, d),
initializationValue
);
${B}
}
}
setOutput(${D});
}
`}}class SS{constructor(e,t,n,s=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");const o=e.filterWidth,a=e.strideDepth,c=e.strideHeight,u=e.strideWidth,p=e.dilationDepth,m=e.dilationHeight,y=e.dilationWidth,b=e.effectiveFilterDepth,w=e.effectiveFilterHeight,I=e.effectiveFilterWidth,T=e.padInfo.front,v=e.padInfo.top,N=e.padInfo.left;this.outputShape=e.outShape;const E=t==="avg";let D="0.0";if(E||(D="-1.0 / 1e-20"),n){const q=">=";this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${u});
const ivec3 pads = ivec3(${T}, ${v}, ${N});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${b};
wD += ${p}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${w};
wR += ${m}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${I};
wC += ${y}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${q} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?i?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${w} * ${I} +
wR * ${I} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const F="max";let _=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");const B=Math.floor(o/4)*4,U=o%4,Y=`
if (${E}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${F}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${u});
const ivec3 pads = ivec3(${T}, ${v}, ${N});
const float initializationValue = ${D};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${D});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${b};
wD += ${p}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${w};
wR += ${m}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${B}; wC += 4) {
int xC = xCCorner + wC * ${y};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${y}, ch),
getValue(batch, xD, xR, xC + 2 * ${y}, ch),
getValue(batch, xD, xR, xC + 3 * ${y}, ch)
);
${Y}
}
int xC = xCCorner + ${B};
if (${U===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${Y}
} else if (${U===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${y}, ch),
initializationValue,
initializationValue
);
${Y}
} else if (${U===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${y}, ch),
getValue(batch, xD, xR, xC + 2 * ${y}, ch),
initializationValue
);
${Y}
}
}
setOutput(${_});
}
}
`}}class AC{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:i,outSize:o}=e;this.outputShape=[s,o];let a="0.0",c="";t==="prod"?a="1.0":t==="min"?(a="1.0 / 1e-20",c="min"):t==="max"&&(a="-1.0 / 1e-20",c="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");const p=Math.floor(n/4)*4,m=n%4;let y=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${c}(values, minMaxValue);
}
`,b="vec4";t==="all"?(a="1.0",y=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,b="bvec4"):t==="any"&&(a="0.0",y=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,b="bvec4");let w="";i%n>0&&(w=`
if (inIdx < 0 || inIdx >= ${i}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${a};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${w}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${a});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${p}; i += 4) {
int inIdx = inOffset + i;
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${y}
}
int inIdx = inOffset + ${p};
if (${m===1}) {
${b} values = ${b}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${y}
} else if (${m===2}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${y}
} else if (${m===3}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${y}
}
setOutput(${u});
}
`}}class vC{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let s=0;s<4;s++){let i="thisRC = rc;";s%2===1&&(i+="thisRC.z += 1;"),s>1&&(i+="thisRC.y += 1;"),n+=`
${i}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${V8(t)}
${yS(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}}function V8(e){const t=Xo(["r","c","d"],e);return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t}
return ivec3(r, c, d);
}
`}class H8{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,s,i]=t.shape,[,o,a]=e.shape,c=[n&&o>1?s-1:s,n&&a>1?i-1:i],u=[n&&o>1?o-1:o,n&&a>1?a-1:a],p=c[0]/u[0],m=c[1]/u[1],y=1/p,b=1/m,w=Math.ceil(y)*2+2,I=Math.ceil(b)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${p});
const float widthScale = float(${m});
const float invHeightScale = float(${y});
const float invWidthScale = float(${b});
const int winHeight = int(${w});
const int winWidth = int(${I});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${o}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${a}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${i-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}}class Y8{constructor(e,t,n,s){this.variableNames=["A"],this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const u=[s&&t>1?o-1:o,s&&n>1?a-1:a],p=[s&&t>1?t-1:t,s&&n>1?n-1:n];this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/p[0]},
${u[1]/p[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${a}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}}class q8{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const u=[s&&t>1?o-1:o,s&&n>1?a-1:a],p=[s&&t>1?t-1:t,s&&n>1?n-1:n];this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/p[0]},
${u[1]/p[1]},
${u[1]/p[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${a}.0,
${a}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${c-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}}class j8{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,s,i]=t.shape,[,o,a]=e.shape,c=[n&&o>1?s-1:s,n&&a>1?i-1:i],u=[n&&o>1?o-1:o,n&&a>1?a-1:a],p=c[0]/u[0],m=c[1]/u[1],y=1/p,b=1/m,w=Math.ceil(y)*2+2,I=Math.ceil(b)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${p});
const float widthScale = float(${m});
const float invHeightScale = float(${y});
const float invWidthScale = float(${b});
const int winHeight = int(${w});
const int winWidth = int(${I});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${o}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${a}) {
continue;
}
float sourceFracRow =
float(${c[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${c[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${i}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}}class K8{constructor(e,t,n,s){this.variableNames=["A"],this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const u=[s&&t>1?o-1:o,s&&n>1?a-1:a],p=[s&&t>1?t-1:t,s&&n>1?n-1:n],m=s?"0.5":"0.0";this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/p[0]},
${u[1]/p[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${a}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}}class X8{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}const s=a=>t.indexOf(a)!==-1&&e[a]!==1?`${e[a]} - coords[${a}] - 1`:`coords[${a}]`,i=e.map((a,c)=>s(c)).join(","),o=Et(n);this.userCode=`
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}}class J8{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const s=us("rc",n),i=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,o=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Et(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${i}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${c(s.slice())};
if(${i}){
result.g = ${u(s.slice())};
}
if(${o}) {
result.b = ${p(s.slice())};
if(${i}) {
result.a = ${m(s.slice())};
}
}
setOutput(result);
}
`;function c(w){return y(w)}function u(w){return w[n-1]="("+w[n-1]+" + 1)",y(w)}function p(w){return w[n-2]="("+w[n-2]+" + 1)",y(w)}function m(w){return w[n-1]="("+w[n-1]+" + 1)",w[n-2]="("+w[n-2]+" + 1)",y(w)}function y(w){const I=e.map((N,E)=>b(E,w)),T=I.join(","),v=I.slice(-2).join(",");return`getChannel(getX(${T}), vec2(${v}))`}function b(w,I){return t.indexOf(w)!==-1&&e[w]!==1?`${e[w]} - ${I[w]} - 1`:`${I[w]}`}}}class NC{constructor(e,t,n,s,i,o,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=o;const c=Et(i.length),u=Et(o.length);let p="";n===1?p="i":n===2&&(p="i, j");const m=`getIndices(${p})`;let y="";s===1?y="i":s===2&&(y="i, coords[1]");const b=`getUpdates(${y})`,w=t>1?"strides[j]":"strides";this.userCode=`
${c} strides = ${c}(${i});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${m});
flattenedIndex += index * ${w};
}
if (flattenedIndex == coords[0]) {
sum += ${b};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}}class Z8{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,s=e.batchSize,i=e.inSize,o=e.numSegments,a=o*Math.ceil(i/n);this.outputShape=[s,a];const c="0.0",u="sumValue",p=Math.floor(n/4)*4,m=n%4,y=`
sumValue += dot(values, segFilter);
`;let b="";i%n>0&&(b=`
if (inIdx < 0 || inIdx >= ${i}) {
return initializationValue;
}
`);let w="";i%n>0&&(w=`
if (inIdx < 0 || inIdx >= ${i}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${c};
float getValue(int batch, int inIdx) {
${b}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${w}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${o})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${o})));
float sumValue = 0.0;
for (int i = 0; i < ${p}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${y}
}
int inIdx = inOffset + ${p};
if (${m===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${y}
} else if (${m===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${y}
} else if (${m===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${y}
}
setOutput(${u});
}
`}}class Q8{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,i;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)i="resRC",s="resRC";else{const a=["resRC.x","resRC.y","resRC.z","resRC.w"],c=[],u=[];for(let p=0;p<t.length;p++)u.push(`${a[p]}`),p<e&&c.push(`${a[p]}`);s=c.join(),i=u.join()}const o=Et(n);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${i}));
} else {
setOutput(getB(${i}));
}
}
`}}class e6{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Et(this.rank),n=`uniform int start[${this.rank}];`,s=t6(this.rank);let i;const o=e.map((a,c)=>`sourceLoc.${IS[c]} = start[${c}] + coords.${IS[c]};`);i=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${o.join(`
`)}
`,this.userCode=`
${n}
void main() {
${i}
setOutput(getSource(${s}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}const IS=["x","y","z","w","u","v"];function t6(e){if(e===1)return"sourceLoc";if(e<=6)return IS.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class n6{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Et(this.rank),n=us("coords",this.rank),s=us("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,o=`getChannel(getSource(${s.join()}), ${i})`,a=`
result.x = ${o};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${o};
--${s[this.rank-1]};
}
`,c=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${o};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${o};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((p,m)=>`start[${m}]`).join()});`:e.map((p,m)=>`${s[m]} = ${n[m]} + start[${m}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${a}
${c}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class s6{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,i=Et(n.length),o=Et(n.length);let a="";if(s===1)a="coords * strides + begin";else{let c=0;a=n.map((u,p)=>(c++,n.length===1?`coords * strides[${p}] + begin[${p}]`:`coords[${c-1}] * strides[${p}] + begin[${p}]`)).join(",")}this.userCode=`
${i} begin = ${i}(${e});
${i} strides = ${i}(${t});
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}}class i6{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=RC(t,n),i=OC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=CC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===In.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===In.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===In.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=RC(n,s),o=OC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=CC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const u=this.usedTextures[o],p=u.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(p,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function r6(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function CC(e,t,n,s,i){const o=o6(t,s);let a;if(i){const[u,p]=fc(e[0],e[1]);a=u*p}else{const[u,p]=cu(e[0],e[1]);a=u*p}const c=r6(n,o);return a*c}function o6(e,t){switch(e){case In.PACKED_2X2_FLOAT32:return IC(t);case In.PACKED_2X2_FLOAT16:return xC(t);case In.UNPACKED_FLOAT32:return wC(t);case In.UNPACKED_FLOAT16:return LC(t);case In.PACKED_4X1_UNSIGNED_BYTE:return SC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function a6(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?In.PACKED_2X2_FLOAT32:In.UNPACKED_FLOAT32:e?In.PACKED_2X2_FLOAT16:In.UNPACKED_FLOAT16}function RC(e,t){if(e===Cs.UPLOAD)return In.PACKED_2X2_FLOAT32;if(e===Cs.RENDER||e==null)return a6(t);if(e===Cs.DOWNLOAD||e===Cs.PIXELS)return In.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function OC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class c6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=l6(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function l6(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let i=0;i<e.length;i++)s.push(`imod(${n[i]}, ${e[i]})`);return s.join()}class it{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}}const dr="if (isnan(x)) return x;",h6="return x;",EC="return abs(x);",DC=dr+`
return (x < 0.0) ? 0.0 : x;
`,kC=dr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,FC="return (x >= 0.0) ? x : (exp(x) - 1.0);",u6=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${xp};
float scale = ${Tp};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function d6(e=0){return dr+`
return x > 0.0 ? 1.0 : float(${e});
`}const _C="return -x;",WC="return ceil(x);",$C="return floor(x);",p6=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,m6="return float(isnan(x));",f6="return float(isinf(x));",g6="return float(!isnan(x) && !isinf(x));",y6=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,UC="return exp(x);",BC="return exp(x) - 1.0;",b6=`if (x < 0.0) return NAN;
return log(x);`,w6="return log(1.0 + x);",L6="return sqrt(x);",S6="return inversesqrt(x);",I6="return 1.0 / (1.0 + exp(-1.0 * x));",x6=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,T6=dr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,A6=dr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,v6=dr+`
return atan(x);
`,N6=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,C6=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,R6=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,O6=dr+"return log(x + sqrt(x * x + 1.0));",E6=dr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,D6=dr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,k6=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${Xb};
float a1 = ${Jb};
float a2 = ${Zb};
float a3 = ${Qb};
float a4 = ${ew};
float a5 = ${tw};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,F6="return 1.0 / x;",_6="return float(!(x >= 1.0));",W6="return float(int(x));",Im="return x;";const $6="return x;",U6=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,MC=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,PC=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,zC=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`;class pu{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}}class B6{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=us("rc",t),s=Et(t),i=xK(t,n),o=n.slice(-2),a=t<=1?"rc":`vec2(${o.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${i});
setOutput(getChannel(packedInput, ${a}));
}
`}}const{segment_util:GC}=iw,M6=rw,P6=ow,z6=aw,G6=dp,V6=1e-7,H6=1e-4,xm={};function Y6(e){return e in xm||(xm[e]={}),xm[e]}function Tm(e,t=!1){if(e==="linear")return t?$6:h6;if(e==="relu")return t?MC:DC;if(e==="elu")return t?zC:FC;if(e==="relu6")return t?PC:kC;if(e==="prelu")return t?hC:lC;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const q6=128,j6=600;function K6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*j6/1024/1024}const VC=1e3;class X6 extends f{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Mi(C().getNumber("WEBGL_VERSION"));this.binaryCache=Y6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new T8(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new i6(this.gpgpu),this.numMBBeforeWarning=K6(),this.texData=new d(this,$s())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Cs.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Cs.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new pu(a,Im):y=new it(a,Im);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const u=this.activeTimers!=null;let p;u&&(p=qn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=ir(y,b)}else m=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=qn()-p),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(I=>w.push(I))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new pu(s,Im):w=new it(s,Im);const I=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(I.dataId);return this.disposeIntermediateTensorInfo(I),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(e);const w=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(w.texture,...lu(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),I=w[0],T=w[1];m=ir(I,T)}else if(u==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(u,w)}p!=null&&this.disposeIntermediateTensorInfo(p);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!_j(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...lu(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?fS(t):t,c=o?new t8(a):new e8(a),u=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),p=this.texData.get(u.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(u),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=Ji(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=Ji(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=iT(c),a.getExtraProfileInfo=()=>c.map((u,p)=>({name:o[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:qn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=qn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,u=this.dataRefCount.get(c);u>1?this.dataRefCount.set(c,u-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const p=this.texData.get(e);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=$s().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=q6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:$s().keep(e.clone()),imag:$s().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=yK(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Xy(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new n6(n):new e6(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=Jy(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=Wd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new s6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new J8(e.shape,t):new X8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(u=>Fo(u)),c=e.map(u=>Ga(u));return Ci(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),u=this.concat(e.slice(a),t);return this.concat([c,u],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new P5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Ur(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new M5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,_C,e.dtype);const n=new it(e.shape,_C);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>VC){n&&(e=Pe(e,[0,2,1])),s&&(t=Pe(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const u=Cn(e.dtype,t.dtype),p=new LS(e.shape,[c,i,o],n,s);return this.compileAndRun(p,[e,t],u)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],u=s?t.shape[1]:t.shape[2],[p,,]=e.shape,m=Cn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?Tm(o,!0):null,I=new LS(e.shape,[p,c,u],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(I,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new oC(rC.REAL,e.shape,t.shape),c=new oC(rC.IMAG,e.shape,t.shape),u=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],p=this.compileAndRun(a,u),m=this.compileAndRun(c,u),y=this.complex(p,m);return p.dispose(),m.dispose(),y}const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=fK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,cC,e.dtype);const s=new un(cC,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new D8(e.shape,t,n,s,i):new O8(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new E8(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>Yl(a)),o=Qe(e.shape,e.dtype,i);return P6(o,t)}const n=new c6(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new G8(e.shape,t,n):new z8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new r8(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((p,m)=>p*m),i=Rh(e.shape,t,s),o=Oh(i.length,t.length),a=Eh(e.shape,t,s),c=jb(n,t.length),u=Kb(a,n,t.length);return Pe(e.reshape(i),o).reshape(a).slice(c,u)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=Rh(o.shape,t,s,!1),c=Oh(a.length,t.length,!1),u=Eh(o.shape,t,s,!1),p=Pe(o.reshape(a),c);return K(p,u)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=uh(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},u=new AC(c,t),p=this.compileAndRun(u,[e],n);return p.shape[1]===1?p:this.reduce(p,t,n)}argReduce(e,t,n=null){let s=e.shape[0],i=e.shape[1];n!=null&&(s=n.shape[0],i=n.shape[1]);const o=uh(i),a={windowSize:o,inSize:i,batchSize:s,outSize:Math.ceil(i/o)},c=new IK(a,t,n==null),u=[e];n!=null&&u.push(n);const p=this.compileAndRun(c,u,"int32");return p.shape[1]===1?p:this.argReduce(e,t,p)}argReducePacked(e,t,n=null){const s=n!=null?n.shape:e.shape,i=s[s.length-1],o=uh(i),a=new o5(s,o,t,n==null),c=n==null?[e]:[e,n],u=this.compileAndRun(a,c,"int32");return u.rank===e.rank?this.argReducePacked(e,t,u):u}sum(e,t){ss("sum",t,e.rank);const[n,s]=On(e.shape,t),i=we(s),o=e.as2D(-1,i),a=vd(e.dtype);return this.reduce(o,"sum",a).reshape(n)}prod(e,t){const n=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.prod(e,t));if(n)return n;const[s,i]=On(e.shape,t),o=we(i),a=e.as2D(-1,o),c=vd(e.dtype);return this.reduce(a,"prod",c).reshape(s)}unsortedSegmentSum(e,t,n){let s=0;const i=_n([s],e.rank);let o=e;i!=null&&(o=Pe(e,i),s=Is(1,e.rank)[0]);const a=GC.computeOutShape(o.shape,s,n),c=we([o.shape[s]]),u=o.as2D(-1,c),p=vd(e.dtype);let m=this.segOpCompute(u,"unsortedSegmentSum",t,p,n).reshape(a);return i!=null&&(m=Pe(m,eh(i))),m}segOpCompute(e,t,n,s,i){const o=e.shape[0],a=e.shape[1],c=GC.segOpComputeOptimalWindowSize(a,i),u={windowSize:c,inSize:a,batchSize:o,numSegments:i},p=new Z8(u,t),m=this.compileAndRun(p,[e,n],s);return m.shape[1]===i?m:(n=yh(0,i).tile([a/c]),this.segOpCompute(m,t,n,s,i))}argMinMaxReduce(e,t,n){const s=[t];if(ss("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.rank),!C().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,o]=On(e.shape,s),a=we(o),c=e.as2D(-1,a);return this.argReduce(c,n).reshape(i)}return this.argReducePacked(e,n)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,n,s){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let o=e;for(let a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new mC(e.shape,!1,s),u=c.getCustomSetupFunc(a),p=o;o=this.compileAndRun(c,[o],o.dtype,u),p.dispose()}if(n){const a=new mC(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,v5,"bool");const n=new un(u5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,N5,"bool");const n=new un(d5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,C5,"bool");const s=new un(p5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,R5,"bool");const n=new un(m5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,O5,"bool");const s=new un(f5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,E5,"bool");const n=new un(g5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new it(e.shape,_6);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,D5,"bool");const n=new un(y5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,k5,"bool");const n=new un(b5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new Q8(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],Cn(t.dtype,n.dtype))}where(e){Qa("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return G6(e.shape,t)}topk(e,t,n){const s=e.dataSync();return z6(s,e.shape,e.dtype,t,n)}min(e,t){ss("min",t,e.rank);const[n,s]=On(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"min",o.dtype).reshape(n)}minimum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.minimum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(_5,e.shape,t.shape):new un(L5,e.shape,t.shape);return this.compileAndRun(s,[e,t])}mod(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(W5,e.shape,t.shape):new un(S5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}maximum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.maximum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(F5,e.shape,t.shape):new un(w5,e.shape,t.shape);return this.compileAndRun(s,[e,t])}all(e,t){ss("all",t,e.rank);const[n,s]=On(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){ss("any",t,e.rank);const[n,s]=On(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"any",o.dtype).reshape(n)}floorDiv(e,t){const n=l5,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,x5,s);const i=new un(n,e.shape,t.shape);return this.compileAndRun(i,[e,t],s)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,bS);const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=cK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,bS,n);const s=new un(bS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new pu(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new to(n,e.shape,t.shape,i);return this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(u=>{const[p,m]=u,y=this.makeComplexComponentTensorInfo(e,p),b=this.makeComplexComponentTensorInfo(t,m),w=new un(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],Cn(p.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>Cn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new SK(e[0].shape,n):new LK(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,wS);const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=bK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,wS,e.dtype);const s=new un(wS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new to(T5,e.shape,t.shape):new un(h5,e.shape,t.shape),i=Cn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=lK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,WC,e.dtype);const t=new it(e.shape,WC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=dK(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new pu(e.shape,MC):t=new it(e.shape,DC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new pu(e.shape,PC):t=new it(e.shape,kC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(hC,e.shape,t.shape):new un(lC,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,zC,e.dtype);const t=new it(e.shape,FC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(A5,e.shape,t.shape):new un(I5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new it(e.shape,u6);return this.compileAndRun(t,[e])}int(e){const t=new it(e.shape,W6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new U5(e.shape):s=new $5(e.shape);const i=s.getCustomSetupFunc(t,n);return this.compileAndRun(s,[e],null,i)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const n=aK(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,EC,e.dtype);const t=new it(e.shape,EC);return this.compileAndRun(t,[e])}complexAbs(e){const t=this.texData.get(e.dataId),n=new B5(e.shape),s=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(n,s)}sigmoid(e){const t=new it(e.shape,I6);return this.compileAndRun(t,[e])}softplus(e){const t=new it(e.shape,x6);return this.compileAndRun(t,[e])}asin(e){const t=new it(e.shape,T6);return this.compileAndRun(t,[e])}acos(e){const t=new it(e.shape,A6);return this.compileAndRun(t,[e])}atan(e){const t=new it(e.shape,v6);return this.compileAndRun(t,[e])}sinh(e){const t=new it(e.shape,N6);return this.compileAndRun(t,[e])}cosh(e){const t=new it(e.shape,C6);return this.compileAndRun(t,[e])}tanh(e){const t=new it(e.shape,R6);return this.compileAndRun(t,[e])}asinh(e){const t=new it(e.shape,O6);return this.compileAndRun(t,[e])}acosh(e){const t=new it(e.shape,E6);return this.compileAndRun(t,[e])}atanh(e){const t=new it(e.shape,D6);return this.compileAndRun(t,[e])}erf(e){const t=new it(e.shape,k6);return this.compileAndRun(t,[e])}step(e,t){const n=new it(e.shape,d6(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),u=n.inChannels,p=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,I=(p===1||m===1)&&u>VC,T=a[2]%2!==0&&!!c.isPacked;if(I||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],U=K(e,[1,B,n.inChannels]),Y=K(t,[1,n.inChannels,n.outChannels]),q=this.fusedBatchMatMul({a:U,b:Y,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o});return K(q,n.outShape)}const v=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(gm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,$s().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:u,outWidth:p,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*u,I=m*p,T=[w,I],v=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new R8(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,U=o!=null,Y=i?Tm(i,!0):null,q=new LS(_.shape,[1,I,n.outChannels],v,N,B,Y,U),J=[_,D];s&&J.push(s),U&&J.push(o);const oe=this.compileAndRun(q,J);return b?oe.reshape([1,m,p,n.outChannels]):oe.reshape([1,n.outChannels,m,p])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,u=i?Tm(i,!1):null,p=new uC(n,a,u,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(p,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n);const s=new uC(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new G5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new z5(n);return this.compileAndRun(s,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){const a=C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1,c=i?Tm(i,a):null,u=[e,t],p=s!=null,m=o!=null;p&&u.push(s),m&&u.push(o);let y;return a?(y=new pC(n,p,c,m),this.compileAndRun(y,u)):(y=new dC(n,p,c,m),this.compileAndRun(y,u))}depthwiseConv2D(e,t,n){let s;return C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1?(s=new pC(n),this.compileAndRun(s,[e,t])):(s=new dC(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new q5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new Y5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new j5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new H5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new V5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return ov(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let u=0;u<e.rank;u++)u!==t&&(s[i++]=e.shape[u]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let u=0;u<c.length;u++)o[t]=u,c[u]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new SS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new c5(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new SS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new SS(s,"max",i),a=this.compileAndRun(o,[t]),c=new F8(s),u=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),u}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new 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this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return $s().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new B6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new $8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[gc(e.shape),...yc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[gc(t),...yc(t)],o=new vC(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=fS(s);let a;n?a=new J5(o):a=new X5(o);const c=!0,u=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===au.DENSE){const I=lu(e.outputShape);a.texShape=I.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=wn(o.dtype,0),o;const c=[],u=t.map(I=>{if(I.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(I.dataId);if(T.texture==null){if(!e.packedInputs&&we(I.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:I.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=I.shape)}else if(!!T.isPacked!==!!e.packedInputs)I=T.isPacked?this.unpackTensor(I):this.packTensor(I),c.push(I),T=this.texData.get(I.dataId);else if(T.isPacked&&!gm(T.shape,I.shape)){const v=I,N=I.shape;I.shape=T.shape,I=this.packedReshape(I,N),c.push(I),T=this.texData.get(I.dataId),v.shape=N}return this.uploadToGPU(I.dataId),{shape:I.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const p={shape:o.shape,texData:a,isUniform:!1},m=C8(e,u,p),y=this.getAndSaveBinary(m,()=>v8(this.gpgpu,e,u,p)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),N8(this.gpgpu,y,u,p,s),c.forEach(I=>this.disposeIntermediateTensorInfo(I)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const I=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),I}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return $s().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?V6:H6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const u=this.activeTimers!=null;let p;u&&(p=qn());let m=t.texShape;if(m==null&&(m=Qj(n,c),t.texShape=m),i!=null){const y=fS(n);let b,w=m[1],I=m[0];const T=i instanceof Uint8Array;c?([w,I]=fc(m[0],m[1]),b=new s8(y,[I,w],T)):b=new n8(y,[I,w],T);const v=this.makeTensorInfo([I,w],s);T?this.texData.get(v.dataId).usage=Cs.PIXELS:this.texData.get(v.dataId).usage=Cs.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(v.dataId),w,I,i);const N=!0,E=this.runWebGLProgram(b,[v],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(v),this.texData.delete(E.dataId),t.values=null,u&&(this.uploadWaitMs+=qn()-p)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=J6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*Ty(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function J6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const Z6="2.6.0";function Q6(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}Fy()&&tb("webgl",()=>new X6,2);const dte={forceHalfFloat:Q6},HC="if (isnan(x)) return x;",e7=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,t7=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Am(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new it(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function xS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,u=o,p=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(t,a.shape,c.shape,!!n):new un(e,a.shape,c.shape),m=s||a.dtype,y=u.runWebGLProgram(p,[a,c],m);return y}}const n7=e7+`
return atan(a, b);
`,s7=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+t7+`
return result;
`,i7=xS(n7,s7),r7={kernelName:Ai,backendName:"webgl",kernelFunc:i7};function TS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const o7={kernelName:Sl,backendName:"webgl",kernelFunc:TS};function a7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;hu(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:u}=s,p=1;k(on(a,p),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${p}'`);const m=Wn(i.shape,o,a,p,c,u);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return TS({inputs:{x:i},backend:n});const y=new du(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const c7={kernelName:ei,backendName:"webgl",kernelFunc:a7};function l7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;hu([i,o],"avgPoolBackprop");const{filterSize:c,strides:u,pad:p}=s,m=Wn(a.shape,c,u,1,p),y=new a5(m);return n.runWebGLProgram(y,[i],a.dtype)}const h7={kernelName:xa,backendName:"webgl",kernelFunc:l7};class u7{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],nt(e,t),nt(e,n);let a="0.0";s!=null&&(nt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(nt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${a};
float scale = ${c};
float inv = scale * inversesqrt(variance + float(${o}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}}class d7{constructor(e,t,n,s,i,o){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],nt(e,t),nt(e,n);let a="vec4(0.0)";s!=null&&(nt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="vec4(1.0)";i!=null&&(nt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${a};
vec4 scale = ${c};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${o}));
setOutput((x - mean) * inv + offset);
}
`}}const p7=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;k(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(c==null||i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);const p=[s,i,o];let m=null;a!=null&&(m=a.shape,p.push(a));let y=null;c!=null&&(y=c.shape,p.push(c));const b=C().getBool("WEBGL_PACK_NORMALIZATION")?new d7(s.shape,i.shape,o.shape,m,y,u):new u7(s.shape,i.shape,o.shape,m,y,u),w=t.runWebGLProgram(b,p,p[0].dtype);return w},m7={kernelName:Ll,backendName:"webgl",kernelFunc:p7};const f7=HC+`
return cos(x);
`,g7=Am(f7),y7={kernelName:Ta,backendName:"webgl",kernelFunc:g7};const b7=`
if (a == b) {
return 1.0;
};
return a / b;`,w7=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,L7=xS(b7,w7,!0),S7={kernelName:Aa,backendName:"webgl",kernelFunc:L7};class I7{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}}const x7={kernelName:rd,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,i=new I7(n.shape),o=s.runWebGLProgram(i,[n],n.dtype);return o}};class T7{constructor(e){this.variableNames=["A"];const t=Un(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}}class A7{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=Un(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}}const v7={kernelName:yd,backendName:"webgl",kernelFunc:N7};let Ic;function N7(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[u,p]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[p,u],y=[p,u,o];(c||a)&&(Ic==null&&(Ic=document.createElement("canvas").getContext("2d")),Ic.canvas.width=u,Ic.canvas.height=p,Ic.drawImage(i,0,0,u,p),i=Ic.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=Cs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new A7(y):new T7(y),I=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),I}function C7(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=uh(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function R7(e,t,n,s){const i=C7(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:u,outSize:p}=i[a],m=new AC({windowSize:u,inSize:c,batchSize:e.shape[0],outSize:p},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function O7(e,t,n){const s=[gc(e.shape),...yc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[gc(t),...yc(t)],a=new vC(o,s),c=!0,u=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function AS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),u=Id(o,c),p=we(u);k(c===p,()=>`The new shape (${u}) has ${p} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!gm(i.shape,u)&&!(m.texture!==null&&gm(m.shape,u))?O7(i,u,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:u,dtype:i.dtype})}const E7={kernelName:El,backendName:"webgl",kernelFunc:AS};function D7(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=AS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),u=R7(c,e.dtype,"max",s),p=AS({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(u),p}class k7{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=F7(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function F7(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let i=0;i<e.length;i++)s[e[i]]=n[i];return s.join()}class _7{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let p=0;p<n.length;p++)n[p]=e[t[p]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const s=Et(this.rank),i=eC("rc",this.rank),o=new Array(this.rank);for(let p=0;p<t.length;p++)o[t[p]]=i[p];const a=`vec2(${o.slice(-2).join()})`,c=`++${i[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${o.join()}), ${a})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${c}) {
result[1] = ${u};
}
--${i[this.rank-1]};
if(++${i[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${c}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}}function YC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _7(e.shape,t):new k7(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const W7={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,u=ft(i,s.shape);let p=u;const m=_n(p,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let U=0;U<F.length;U++)F[U]=s.shape[m[U]];const _=Q0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=YC(s,m,a);p=Is(p.length,c)}ss("max",p,c);const[I,T]=On(w.shape,p);let v=I;o&&(v=En(I,u));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=mK(D,we(T),v,s.dtype);N=a.makeTensorInfo(v,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=D7(w,T,v,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function $7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;hu(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:u}=s,p=1;k(on(a,p),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${p}'`);const m=Wn(i.shape,o,a,p,c,u);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return TS({inputs:{x:i},backend:n});const y=new du(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const U7={kernelName:Cl,backendName:"webgl",kernelFunc:$7};function B7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;hu([o,a],"maxPoolBackprop");const{filterSize:u,strides:p,pad:m,dimRoundingMode:y}=s,b=Wn(c.shape,u,p,1,m,y),w=!0,I=new du(b,"max",w),T=n.runWebGLProgram(I,[c],c.dtype),v=new k8(b),N=n.runWebGLProgram(v,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const M7={kernelName:ad,backendName:"webgl",kernelFunc:B7};function P7(e,t,n,s){let i=new du(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new du(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const z7={kernelName:cd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,u=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const p=[1,1];k(on(o,p),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);const m=Wn(s.shape,i,o,p,a),[y,b]=P7(s,c,m,u);return[y,b]}};const G7={kernelName:Kg,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,u=t,p=u.readSync(s.dataId),m=u.readSync(i.dataId),y=o,b=a,w=c;return wp(p,m,y,b,w)}};const V7=Lp,H7={kernelName:hd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:u}=n,p=t,m=p.readSync(s.dataId),y=p.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=V7(m,y,o,a,c,u);return[b,w]}};const Y7=Sp,q7={kernelName:ud,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:u}=n,p=t,m=p.readSync(s.dataId),y=p.readSync(i.dataId),b=o,w=a,I=c,T=u,{selectedIndices:v,selectedScores:N}=Y7(m,y,b,w,I,T);return[v,N]}};class j7{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[u,p]=qb(s,i,o),m=u.toFixed(3),y=p.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
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void main() {
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int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - ${m}) * ${c} - (float(y) - ${y}) * ${a};
float coordYFloat = (float(x) - ${m}) * ${a} + (float(y) - ${y}) * ${c};
int coordX = int(round(coordXFloat + ${m}));
int coordY = int(round(coordYFloat + ${y}));
${b}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${i}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
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Rc(r){return gi(r)&&0<=r&&r<=1}class Ze{constructor(r,l){this._x=r,this._y=l}get x(){return this._x}get y(){return this._y}add(r){return new Ze(this.x+r.x,this.y+r.y)}sub(r){return new Ze(this.x-r.x,this.y-r.y)}mul(r){return new Ze(this.x*r.x,this.y*r.y)}div(r){return new Ze(this.x/r.x,this.y/r.y)}abs(){return new Ze(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(Math.pow(this.x,2)+Math.pow(this.y,2))}floor(){return new Ze(Math.floor(this.x),Math.floor(this.y))}}class Ct{static isRect(r){return!!r&&[r.x,r.y,r.width,r.height].every(gi)}static assertIsValidBox(r,l,h=!1){if(!Ct.isRect(r))throw new Error(`${l} - invalid box: ${JSON.stringify(r)}, expected object with properties x, y, width, height`);if(!h&&(r.width<0||r.height<0))throw new Error(`${l} - width (${r.width}) and height (${r.height}) must be positive numbers`)}constructor(r,l=!0){const h=r||{},d=[h.left,h.top,h.right,h.bottom].every(gi),f=[h.x,h.y,h.width,h.height].every(gi);if(!f&&!d)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(h)}`);const[g,S,L,x]=f?[h.x,h.y,h.width,h.height]:[h.left,h.top,h.right-h.left,h.bottom-h.top];Ct.assertIsValidBox({x:g,y:S,width:L,height:x},"Box.constructor",l),this._x=g,this._y=S,this._width=L,this._height=x}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Ze(this.left,this.top)}get topRight(){return new Ze(this.right,this.top)}get bottomLeft(){return new Ze(this.left,this.bottom)}get bottomRight(){return new Ze(this.right,this.bottom)}round(){const[r,l,h,d]=[this.x,this.y,this.width,this.height].map(f=>Math.round(f));return new Ct({x:r,y:l,width:h,height:d})}floor(){const[r,l,h,d]=[this.x,this.y,this.width,this.height].map(f=>Math.floor(f));return new Ct({x:r,y:l,width:h,height:d})}toSquare(){let{x:r,y:l,width:h,height:d}=this;const f=Math.abs(h-d);return h<d&&(r-=f/2,h+=f),d<h&&(l-=f/2,d+=f),new Ct({x:r,y:l,width:h,height:d})}rescale(r){const l=Dm(r)?r.width:r,h=Dm(r)?r.height:r;return new Ct({x:this.x*l,y:this.y*h,width:this.width*l,height:this.height*h})}pad(r,l){let[h,d,f,g]=[this.x-r/2,this.y-l/2,this.width+r,this.height+l];return new Ct({x:h,y:d,width:f,height:g})}clipAtImageBorders(r,l){const{x:h,y:d,right:f,bottom:g}=this,S=Math.max(h,0),L=Math.max(d,0),x=f-S,A=g-L,O=Math.min(x,r-S),C=Math.min(A,l-L);return new Ct({x:S,y:L,width:O,height:C}).floor()}shift(r,l){const{width:h,height:d}=this,f=this.x+r,g=this.y+l;return new Ct({x:f,y:g,width:h,height:d})}padAtBorders(r,l){const h=this.width+1,d=this.height+1;let f=1,g=1,S=h,L=d,x=this.left,A=this.top,O=this.right,C=this.bottom;return O>l&&(S=-O+l+h,O=l),C>r&&(L=-C+r+d,C=r),x<1&&(L=2-x,x=1),A<1&&(L=2-A,A=1),{dy:g,edy:L,dx:f,edx:S,y:A,ey:C,x,ex:O,w:h,h:d}}calibrate(r){return new Ct({left:this.left+r.left*this.width,top:this.top+r.top*this.height,right:this.right+r.right*this.width,bottom:this.bottom+r.bottom*this.height}).toSquare().round()}}class wu extends Ct{constructor(r,l,h,d,f=!1){super({left:r,top:l,right:h,bottom:d},f)}}class Oc{constructor(r,l,h,d,f){this._imageDims=new ms(f.width,f.height),this._score=r,this._classScore=l,this._className=h,this._box=new Ct(d).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new Ct(this._box).rescale(this.imageDims.reverse())}forSize(r,l){return new Oc(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Yt extends Oc{constructor(r,l,h){super(r,r,"",l,h)}forSize(r,l){const{score:h,relativeBox:d,imageDims:f}=super.forSize(r,l);return new Yt(h,d,f)}}function zS(r,l,h=!0){const d=Math.max(0,Math.min(r.right,l.right)-Math.max(r.left,l.left)),f=Math.max(0,Math.min(r.bottom,l.bottom)-Math.max(r.top,l.top)),g=d*f;return h?g/(r.area+l.area-g):g/Math.min(r.area,l.area)}function GS(r){const l=r.map(L=>L.x),h=r.map(L=>L.y),d=l.reduce((L,x)=>x<L?x:L,Infinity),f=h.reduce((L,x)=>x<L?x:L,Infinity),g=l.reduce((L,x)=>L<x?x:L,0),S=h.reduce((L,x)=>L<x?x:L,0);return new wu(d,f,g,S)}function VS(r,l,h,d=!0){let f=l.map((S,L)=>({score:S,boxIndex:L})).sort((S,L)=>S.score-L.score).map(S=>S.boxIndex);const g=[];for(;f.length>0;){const S=f.pop();g.push(S);const L=f,x=[];for(let A=0;A<L.length;A++){const O=L[A],C=r[S],$=r[O];x.push(zS(C,$,d))}f=f.filter((A,O)=>x[O]<=h)}return g}const Gi=Ye(Je());function yi(r,l){return Gi.tidy(()=>{const[h,d,f]=l,g=Gi.fill([...r.shape.slice(0,3),1],h,"float32"),S=Gi.fill([...r.shape.slice(0,3),1],d,"float32"),L=Gi.fill([...r.shape.slice(0,3),1],f,"float32"),x=Gi.concat([g,S,L],3);return Gi.sub(r,x)})}const ro=Ye(Je());function HS(r,l=!1){return ro.tidy(()=>{const[h,d]=r.shape.slice(1);if(h===d)return r;const f=Math.abs(h-d),g=Math.round(f*(l?.5:1)),S=h>d?2:1,L=$=>{const z=r.shape.slice();return z[S]=$,ro.fill(z,0,"float32")},x=L(g),A=f-x.shape[S],O=l&&A?L(A):null,C=[O,r,x].filter($=>!!$).map($=>ro.cast($,"float32"));return ro.concat(C,S)})}function FX(r){const l=r.slice();for(let h=l.length-1;h>0;h--){const d=Math.floor(Math.random()*(h+1)),f=l[h];l[h]=l[d],l[d]=f}return l}function Lu(r){return 1/(1+Math.exp(-r))}function _X(r){return Math.log(r/(1-r))}class Su extends Ct{constructor(r,l,h,d,f=!1){super({x:r,y:l,width:h,height:d},f)}}const WX=.5,$X=.43,UX=.45;class qs{constructor(r,l,h=new Ze(0,0)){const{width:d,height:f}=l;this._imgDims=new ms(d,f),this._shift=h,this._positions=r.map(g=>g.mul(new Ze(d,f)).add(h))}get shift(){return new Ze(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(r=>r.sub(this._shift).div(new Ze(this.imageWidth,this.imageHeight)))}forSize(r,l){return new this.constructor(this.relativePositions,{width:r,height:l})}shiftBy(r,l){return new this.constructor(this.relativePositions,this._imgDims,new Ze(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const f=r instanceof Yt?r.box.floor():new Ct(r);return this.shiftBy(f.x,f.y).align(null,l)}const{useDlibAlignment:h,minBoxPadding:d}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},l);return h?this.alignDlib():this.alignMinBbox(d)}alignDlib(){const r=this.getRefPointsForAlignment(),[l,h,d]=r,f=O=>d.sub(O).magnitude(),g=(f(l)+f(h))/2,S=Math.floor(g/UX),L=ta(r),x=Math.floor(Math.max(0,L.x-WX*S)),A=Math.floor(Math.max(0,L.y-$X*S));return new Su(x,A,Math.min(S,this.imageWidth+x),Math.min(S,this.imageHeight+A))}alignMinBbox(r){const l=GS(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class BX extends qs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],ta([r[3],r[4]])]}}class Iu extends qs{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(ta)}}class km{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${ea(this.distance)})`:""}`}}class Fm extends Ct{static assertIsValidLabeledBox(r,l){if(Ct.assertIsValidBox(r,l),!gi(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class na{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(h=>!(h instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(h=>new Float32Array(h));return new na(r.label,l)}}class MX extends Fm{static assertIsValidPredictedBox(r,l){if(Fm.assertIsValidLabeledBox(r,l),!Rc(r.score)||!Rc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,h,d){super(r,l);this._score=h,this._classScore=d}get score(){return this._score}get classScore(){return this._classScore}}function Vi(r){return r.detection instanceof Yt}function sa(r,l){const h={detection:l};return Object.assign({},r,h)}function YS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:r,readFile:l}}function _m(r){let l="";if(!r)try{r=require("fs")}catch(d){l=d.toString()}const h=r?function(d){return new Promise((f,g)=>{r.readFile(d,function(S,L){return S?g(S):f(L)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${l}`)};return{readFile:h}}function qS(){const r=global.Canvas||global.HTMLCanvasElement,l=global.Image||global.HTMLImageElement,h=function(){if(r)return new r;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},d=function(){if(l)return new l;throw new Error("createImageElement - missing Image implementation for nodejs environment")},f=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},g=_m();return{Canvas:r||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:l||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:h,createImageElement:d,fetch:f,...g}}function jS(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const KS=Ye(a2());let yn;function PX(){if(!yn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return yn}function XS(r){yn=r}function JS(){if(jS())return XS(YS());if(KS.isNodejs())return XS(qS())}function zX(r){if(yn||JS(),!yn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:l=yn.Canvas,Image:h=yn.Image}=r;yn.Canvas=l,yn.Image=h,yn.createCanvasElement=r.createCanvasElement||(()=>new l),yn.createImageElement=r.createImageElement||(()=>new h),yn.ImageData=r.ImageData||yn.ImageData,yn.Video=r.Video||yn.Video,yn.fetch=r.fetch||yn.fetch,yn.readFile=r.readFile||yn.readFile}const gt={getEnv:PX,setEnv:XS,initialize:JS,createBrowserEnv:YS,createFileSystem:_m,createNodejsEnv:qS,monkeyPatch:zX,isBrowser:jS,isNodejs:KS.isNodejs};JS();function ia(r){return!gt.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function es(r){const{Canvas:l,CanvasRenderingContext2D:h}=gt.getEnv();if(r instanceof h)return r;const d=ia(r);if(!(d instanceof l))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const f=d.getContext("2d");if(!f)throw new Error("resolveContext2d - canvas 2d context is null");return f}var Hi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Hi||(Hi={}));class Wm{constructor(r={}){const{anchorPosition:l,backgroundColor:h,fontColor:d,fontSize:f,fontStyle:g,padding:S}=r;this.anchorPosition=l||Hi.TOP_LEFT,this.backgroundColor=h||"rgba(0, 0, 0, 0.5)",this.fontColor=d||"rgba(255, 255, 255, 1)",this.fontSize=f||14,this.fontStyle=g||"Georgia",this.padding=S||4}}class Ec{constructor(r,l,h={}){this.text=typeof r=="string"?[r]:r instanceof Ec?r.text:r,this.anchor=l,this.options=new Wm(h)}measureWidth(r){const{padding:l}=this.options;return this.text.map(h=>r.measureText(h).width).reduce((h,d)=>h<d?d:h,0)+2*l}measureHeight(){const{fontSize:r,padding:l}=this.options;return this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:h}=this.options,d=h===Hi.BOTTOM_RIGHT||h===Hi.TOP_RIGHT,f=h===Hi.BOTTOM_LEFT||h===Hi.BOTTOM_RIGHT,g=this.measureWidth(r),S=this.measureHeight(),L=d?this.anchor.x-g:this.anchor.x,x=f?this.anchor.y-S:this.anchor.y;if(l){const{width:A,height:O}=l,C=Math.max(Math.min(L,A-g),0),$=Math.max(Math.min(x,O-S),0);return{x:C,y:$}}return{x:L,y:x}}draw(r){const l=ia(r),h=es(l),{backgroundColor:d,fontColor:f,fontSize:g,fontStyle:S,padding:L}=this.options;h.font=`${g}px ${S}`;const x=this.measureWidth(h),A=this.measureHeight();h.fillStyle=d;const O=this.getUpperLeft(h,l);h.fillRect(O.x,O.y,x,A),h.fillStyle=f,this.text.forEach((C,$)=>{const z=L+O.x,ne=L+O.y+($+1)*g;h.fillText(C,z,ne)})}}class l2{constructor(r={}){const{boxColor:l,lineWidth:h,label:d,drawLabelOptions:f}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=h||2,this.label=d;const g={anchorPosition:Hi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Wm(Object.assign({},g,f))}}class ZS{constructor(r,l={}){this.box=new Ct(r),this.options=new l2(l)}draw(r){const l=es(r),{boxColor:h,lineWidth:d}=this.options,{x:f,y:g,width:S,height:L}=this.box;l.strokeStyle=h,l.lineWidth=d,l.strokeRect(f,g,S,L);const{label:x}=this.options;x&&new Ec([x],{x:f-d/2,y:g},this.options.drawLabelOptions).draw(r)}}function GX(r,l){const h=Array.isArray(l)?l:[l];h.forEach(d=>{const f=d instanceof Yt?d.score:Vi(d)?d.detection.score:void 0,g=d instanceof Yt?d.box:Vi(d)?d.detection.box:new Ct(d),S=f?`${ea(f)}`:void 0;new ZS(g,{label:S}).draw(r)})}function xu(r){const{Image:l,Video:h}=gt.getEnv();return r instanceof l&&r.complete||r instanceof h&&r.readyState>=3}function QS(r){return new Promise((l,h)=>{if(r instanceof gt.getEnv().Canvas||xu(r))return l(null);function d(g){if(!g.currentTarget)return;g.currentTarget.removeEventListener("load",d),g.currentTarget.removeEventListener("error",f),l(g)}function f(g){if(!g.currentTarget)return;g.currentTarget.removeEventListener("load",d),g.currentTarget.removeEventListener("error",f),h(g)}r.addEventListener("load",d),r.addEventListener("error",f)})}function eI(r){return new Promise((l,h)=>{if(!(r instanceof Blob))return h("bufferToImage - expected buf to be of type: Blob");const d=new FileReader;d.onload=()=>{if(typeof d.result!="string")return h("bufferToImage - expected reader.result to be a string, in onload");const f=gt.getEnv().createImageElement();f.onload=()=>l(f),f.onerror=h,f.src=d.result},d.onerror=h,d.readAsDataURL(r)})}function ra(r){const{Image:l,Video:h}=gt.getEnv();return r instanceof l?new ms(r.naturalWidth,r.naturalHeight):r instanceof h?new ms(r.videoWidth,r.videoHeight):new ms(r.width,r.height)}function Dc({width:r,height:l}){const{createCanvasElement:h}=gt.getEnv(),d=h();return d.width=r,d.height=l,d}function Tu(r,l){const{ImageData:h}=gt.getEnv();if(!(r instanceof h)&&!xu(r))throw new Error("createCanvasFromMedia - media 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ie("batchMatMul")}fusedBatchMatMul({a:r,b:l,transposeA:h,transposeB:d,bias:f,activation:g,preluActivationWeights:S}){return ie("fusedBatchMatMul")}slice(r,l,h){return ie("slice")}stridedSlice(r,l,h,d){return ie("stridedSlice")}unstack(r,l){return ie("unstack")}reverse(r,l){return ie("reverse")}concat(r,l){return ie("concat")}neg(r){return ie("neg")}add(r,l){return ie("add")}addN(r){return ie("addN")}subtract(r,l){return ie("subtract")}multiply(r,l){return ie("multiply")}realDivide(r,l){return ie("realDivide")}floorDiv(r,l){return ie("floorDiv")}sum(r,l){return ie("sum")}prod(r,l){return ie("prod")}unsortedSegmentSum(r,l,h){return ie("unsortedSegmentSum")}argMin(r,l){return ie("argMin")}argMax(r,l){return ie("argMax")}equal(r,l){return ie("equal")}notEqual(r,l){return ie("notEqual")}less(r,l){return ie("less")}lessEqual(r,l){return ie("lessEqual")}greater(r,l){return ie("greater")}greaterEqual(r,l){return ie("greaterEqual")}logicalNot(r){return ie("logicalNot")}logicalAnd(r,l){return 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`;return $[$.length-1]=" "+$[$.length-1]+"]"+(g?"":ne),$}function Ou(r){const l=[];for(let h=0;h<r.length;h+=2)l.push([r[h],r[h+1]]);return l}class eO{constructor(r,l,h){if(this.dtype=l,this.shape=r.slice(),this.size=qt(r),h!=null){const d=h.length;Z(d===this.size,()=>`Length of values '${d}' does not match the size inferred by the shape '${this.size}'.`)}if(l==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=h||zR(l,this.size),this.strides=Nu(r)}set(r,...l){l.length===0&&(l=[0]),Z(l.length===this.rank,()=>`The number of provided coordinates (${l.length}) must match the rank (${this.rank})`);const h=this.locToIndex(l);this.values[h]=r}get(...r){r.length===0&&(r=[0]);let l=0;for(const d of r){if(d<0||d>=this.shape[l]){const f=`Requested out of range element at ${r}. 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To get the original bytes, call tensor.bytes().")}}return r}dataSync(){this.throwIfDisposed();const r=Yi().readSync(this.dataId);if(this.dtype==="string")try{return r.map(l=>dI(l))}catch(l){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return r}async bytes(){this.throwIfDisposed();const r=await Yi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;Yi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Uc.print(this,r)}clone(){return this.throwIfDisposed(),Uc.clone(this)}toString(r=!1){const l=this.dataSync();return ZR(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Uc.cast(this,r)}variable(r=!0,l,h){return this.throwIfDisposed(),Yi().makeVariable(this,r,l,h)}}Object.defineProperty(Tn,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class Pf extends Tn{constructor(r,l,h,d){super(r.shape,r.dtype,r.dataId,d);this.trainable=l,this.name=h}assign(r){if(r.dtype!==this.dtype)throw new Error(`dtype of the new value (${r.dtype}) and previous value (${this.dtype}) must match`);if(!oa(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);Yi().disposeTensor(this),this.dataId=r.dataId,Yi().incRef(this,null)}dispose(){Yi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Pf,Symbol.hasInstance,{value:r=>r instanceof Tn&&r.assign!=null&&r.assign instanceof Function});var iO;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(iO||(iO={}));var mI;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(mI||(mI={}));var fI;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(fI||(fI={}));var gI;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(gI||(gI={}));var yI;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(yI||(yI={}));const rJ={float32:gI,int32:mI,bool:fI,complex64:yI};function rO(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return rJ[r][l]}function mt(r,l){if(r.dtype===l.dtype)return[r,l];const h=rO(r.dtype,l.dtype);return[r.cast(h),l.cast(h)]}function zf(r){const l=[],h=new Set;return oO(r,l,h),l}function oO(r,l,h){if(r==null)return;if(r instanceof Tn){l.push(r);return}if(!oJ(r))return;const d=r;for(const f in d){const g=d[f];h.has(g)||(h.add(g),oO(g,l,h))}}function oJ(r){return Array.isArray(r)||typeof r=="object"}class aO{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class Eu{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new aO}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const h=r[l],d=await this.initializeBackend(h).success;if(d){await this.setBackend(h);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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d=++this.pendingBackendInitId,f=h.then(g=>d<this.pendingBackendInitId?!1:(this.registry[r]=g,this.pendingBackendInit=null,!0)).catch(g=>(d<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(g.stack||g.message)),!1));return this.pendingBackendInit=f,{success:f,asyncInit:!0}}else return this.registry[r]=h,{success:!0,asyncInit:!1}}catch(h){return console.warn(`Initialization of backend ${r} failed`),console.warn(h.stack||h.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${r} backend not found in registry`);this.backendName===r&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,r in this.registry&&(this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r]),delete this.registryFactory[r],this.backendName===r&&(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((r,l)=>this.registryFactory[l].priority-this.registryFactory[r].priority)}initializeBackendsAndReturnBest(){const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const h=r[l],{success:d,asyncInit:f}=this.initializeBackend(h);if(f||d)return{name:h,asyncInit:f}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(r,l){const h=this.state.tensorInfo.get(l),d=h.backend,f=this.readSync(l);d.disposeData(l),h.backend=r,r.move(l,f,h.shape,h.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(r,l){let h=null;if(l==null){if(typeof r!="function")throw new Error("Please provide a function to 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this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,h){const d=this.backend.numDataIds();let f=0;h.forEach(L=>{f+=L.dtype==="complex64"?3:1});const g=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],S=d-l-f-g;if(S>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${S} data ids) after running '${r}'`)}runKernelFunc(r,l,h,d,f,g,S){let L,x=[];const A=this.isTapeOn();d==null&&(d=this.state.activeScope!=null?this.state.activeScope.name:"");const O=this.state.numBytes,C=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let $;const z=Wf(d,this.backendName);let ne;if(z!=null)$=()=>{const se=this.backend.numDataIds();ne=z.kernelFunc({inputs:l,attrs:f,backend:this.backend});const fe=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,se,fe);const de=fe.map(({dataId:Ae,shape:xe,dtype:Me})=>this.makeTensorFromDataId(Ae,xe,Me));if(A){let Ae=this.getTensorsForGradient(d,l,de);if(Ae==null){S==null&&(S=[]);const xe=de.filter((Me,Ke)=>S[Ke]);Ae=(g||[]).slice().concat(xe)}x=this.saveTensorsForBackwardMode(Ae)}return de};else{const se=fe=>{if(!A)return;x=fe.map(de=>this.keep(this.clone(de)))};$=()=>{const fe=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,se));const de=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,fe,de),de}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?L=$():(te=this.profiler.profileKernel(d,l,()=>$()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),L=te.outputs)}),A&&this.addTapeNode(d,l,L,h,x,f),this.state.profiling&&this.state.activeProfile.kernels.push({name:d,bytesAdded:this.state.numBytes-O,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-C,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(se=>l[se]!=null?l[se].shape:null),outputShapes:L.map(se=>se.shape),kernelTimeMs:te.timeMs,extraInfo:te.extraInfo}),Array.isArray(ne)?L:L[0]}saveTensorsForBackwardMode(r){const l=r.map(h=>this.keep(this.clone(h)));return l}getTensorsForGradient(r,l,h){const d=aI(r);if(d!=null){const f=d.inputsToSave||[],g=d.outputsToSave||[];let S;d.saveAllInputs?(Z(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an 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h=this.state.tensorInfo.has(r.dataId)?this.state.tensorInfo.get(r.dataId).refCount:0;if(this.state.numTensors++,r.dtype==="string"&&this.state.numStringTensors++,h===0){this.state.numDataBuffers++;let d=0;r.dtype!=="complex64"&&r.dtype!=="string"&&(d=r.size*VR(r.dtype)),this.state.tensorInfo.set(r.dataId,{backend:l||this.backend,dtype:r.dtype,shape:r.shape,bytes:d,refCount:0}),this.state.numBytes+=d}this.state.tensorInfo.get(r.dataId).refCount++,r instanceof Pf||this.track(r)}disposeTensor(r){if(!this.state.tensorInfo.has(r.dataId))return;this.state.numTensors--,r.dtype==="string"&&this.state.numStringTensors--;const l=this.state.tensorInfo.get(r.dataId),h=l.refCount;h<=1?(r.dtype!=="complex64"&&(this.state.numBytes-=l.bytes),this.state.numDataBuffers--,l.backend.disposeData(r.dataId),this.state.tensorInfo.delete(r.dataId)):this.state.tensorInfo.get(r.dataId).refCount--}disposeVariables(){for(const r in this.state.registeredVariables){const l=this.state.registeredVariables[r];this.disposeVariable(l)}}disposeVariable(r){this.disposeTensor(r),this.state.registeredVariables[r.name]!=null&&delete this.state.registeredVariables[r.name]}memory(){const r=this.backend.memory();return r.numTensors=this.state.numTensors,r.numDataBuffers=this.state.numDataBuffers,r.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(r.unreliable=!0,r.reasons==null&&(r.reasons=[]),r.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),r}async profile(r){this.state.profiling=!0;const l=this.state.numBytes,h=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await r(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-h;for(const d of 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kn{constructor(r){this._name=r;this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(r){const{obj:l,objProp:h}=this.traversePropertyPath(r);return l[h]}reassignParamFromPath(r,l){const{obj:h,objProp:d}=this.traversePropertyPath(r);h[d].dispose(),h[d]=l}getParamList(){return this._paramMappings.map(({paramPath:r})=>({path:r,tensor:this.getParamFromPath(r)}))}getTrainableParams(){return this.getParamList().filter(r=>r.tensor instanceof xr.Variable)}getFrozenParams(){return this.getParamList().filter(r=>!(r.tensor instanceof xr.Variable))}variable(){this.getFrozenParams().forEach(({path:r,tensor:l})=>{this.reassignParamFromPath(r,l.variable())})}freeze(){this.getTrainableParams().forEach(({path:r,tensor:l})=>{const h=xr.tensor(l.dataSync());l.dispose(),this.reassignParamFromPath(r,h)})}dispose(r=!0){this.getParamList().forEach(l=>{if(r&&l.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await rx(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=gt.getEnv(),{manifestUri:h,modelBaseUri:d}=rg(r,this.getDefaultModelName()),f=x=>Promise.all(x.map(A=>l(A).then(O=>O.buffer))),g=xr.io.weightsLoaderFactory(f),S=JSON.parse((await l(h)).toString()),L=await g(S,d);this.loadFromWeightMap(L)}loadFromWeightMap(r){const{paramMappings:l,params:h}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=h}extractWeights(r){const{paramMappings:l,params:h}=this.extractParams(r);this._paramMappings=l,this._params=h}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((f,g)=>{if(!f.nextObj.hasOwnProperty(g))throw new Error(`traversePropertyPath - object does not have property ${g}, for path ${r}`);return{obj:f.nextObj,objProp:g,nextObj:f.nextObj[g]}},{nextObj:this.params}),{obj:h,objProp:d}=l;if(!h||!d||!(h[d]instanceof xr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:h,objProp:d}}}const jc=Ye(Je());function ns(r,l,h){return jc.tidy(()=>{let d=jc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,h,"same");return d=jc.add(d,l.bias),d})}const Dt=Ye(Je());function og(r,l,h=!1){return Dt.tidy(()=>{const d=Dt.relu(h?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):ns(r,l.conv0,[2,2])),f=ns(d,l.conv1,[1,1]),g=Dt.relu(Dt.add(d,f)),S=ns(g,l.conv2,[1,1]);return Dt.relu(Dt.add(d,Dt.add(f,S)))})}function Mu(r,l,h=!1,d=!0){return Dt.tidy(()=>{const f=Dt.relu(h?Dt.add(Dt.conv2d(r,l.conv0.filters,d?[2,2]:[1,1],"same"),l.conv0.bias):ns(r,l.conv0,d?[2,2]:[1,1])),g=ns(f,l.conv1,[1,1]),S=Dt.relu(Dt.add(f,g)),L=ns(S,l.conv2,[1,1]),x=Dt.relu(Dt.add(f,Dt.add(g,L))),A=ns(x,l.conv3,[1,1]);return Dt.relu(Dt.add(f,Dt.add(g,Dt.add(L,A))))})}const mo=Ye(Je());function ga(r,l,h="same",d=!1){return mo.tidy(()=>{const f=mo.add(mo.conv2d(r,l.filters,[1,1],h),l.bias);return d?mo.relu(f):f})}function Vn(r,l){Object.keys(r).forEach(h=>{l.some(d=>d.originalPath===h)||r[h].dispose()})}const ag=Ye(Je());function Kc(r,l){return function(h,d,f,g){const S=ag.tensor4d(r(h*d*f*f),[f,f,h,d]),L=ag.tensor1d(r(d));return l.push({paramPath:`${g}/filters`},{paramPath:`${g}/bias`}),{filters:S,bias:L}}}const cg=Ye(Je());function lg(r,l){return function(h,d,f){const g=cg.tensor2d(r(h*d),[h,d]),S=cg.tensor1d(r(d));return l.push({paramPath:`${f}/weights`},{paramPath:`${f}/bias`}),{weights:g,bias:S}}}class ox{constructor(r,l,h){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=h}}const Pu=Ye(Je());function Xc(r,l){return function(h,d,f){const g=Pu.tensor4d(r(3*3*h),[3,3,h,1]),S=Pu.tensor4d(r(h*d),[1,1,h,d]),L=Pu.tensor1d(r(d));return l.push({paramPath:`${f}/depthwise_filter`},{paramPath:`${f}/pointwise_filter`},{paramPath:`${f}/bias`}),new ox(g,S,L)}}function Jc(r){return function(l){const h=r(`${l}/depthwise_filter`,4),d=r(`${l}/pointwise_filter`,4),f=r(`${l}/bias`,1);return new ox(h,d,f)}}function ys(r,l){return function(h,d,f){const g=r[h];if(!Qo(g,d))throw new Error(`expected weightMap[${h}] to be a Tensor${d}D, instead have ${g}`);return l.push({originalPath:h,paramPath:f||h}),g}}function Hn(r){let l=r;function h(f){const g=l.slice(0,f);return l=l.slice(f),g}function d(){return l}return{extractWeights:h,getRemainingWeights:d}}function hg(r,l){const h=Kc(r,l),d=Xc(r,l);function f(S,L,x,A=!1){const O=A?h(S,L,3,`${x}/conv0`):d(S,L,`${x}/conv0`),C=d(L,L,`${x}/conv1`),$=d(L,L,`${x}/conv2`);return{conv0:O,conv1:C,conv2:$}}function g(S,L,x,A=!1){const{conv0:O,conv1:C,conv2:$}=f(S,L,x,A),z=d(L,L,`${x}/conv3`);return{conv0:O,conv1:C,conv2:$,conv3:z}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:g}}function JE(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),{extractDenseBlock4Params:f}=hg(h,l),g=f(3,32,"dense0",!0),S=f(32,64,"dense1"),L=f(64,128,"dense2"),x=f(128,256,"dense3");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{dense0:g,dense1:S,dense2:L,dense3:x}}}function ug(r){return function(l){const h=r(`${l}/filters`,4),d=r(`${l}/bias`,1);return{filters:h,bias:d}}}function dg(r,l){const h=ys(r,l),d=ug(h),f=Jc(h);function g(L,x=!1){const A=x?d(`${L}/conv0`):f(`${L}/conv0`),O=f(`${L}/conv1`),C=f(`${L}/conv2`);return{conv0:A,conv1:O,conv2:C}}function S(L,x=!1){const A=x?d(`${L}/conv0`):f(`${L}/conv0`),O=f(`${L}/conv1`),C=f(`${L}/conv2`),$=f(`${L}/conv3`);return{conv0:A,conv1:O,conv2:C,conv3:$}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:S}}function ZE(r){const l=[],{extractDenseBlock4Params:h}=dg(r,l),d={dense0:h("dense0",!0),dense1:h("dense1"),dense2:h("dense2"),dense3:h("dense3")};return Vn(r,l),{params:d,paramMappings:l}}const fo=Ye(Je());class pg extends kn{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return fo.tidy(()=>{const h=fo.cast(r.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(fo.scalar(255));let g=Mu(f,l.dense0,!0);return g=Mu(g,l.dense1),g=Mu(g,l.dense2),g=Mu(g,l.dense3),g=fo.avgPool(g,[7,7],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return ZE(r)}extractParams(r){return JE(r)}}const Zc=Ye(Je());function zu(r,l){return Zc.tidy(()=>Zc.add(Zc.matMul(r,l.weights),l.bias))}function QE(r,l,h){const d=[],{extractWeights:f,getRemainingWeights:g}=Hn(r),S=lg(f,d),L=S(l,h,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:d,params:{fc:L}}}function eD(r){const l=[],h=ys(r,l);function d(g){const S=h(`${g}/weights`,2),L=h(`${g}/bias`,1);return{weights:S,bias:L}}const f={fc:d("fc")};return Vn(r,l),{params:f,paramMappings:l}}function mg(r){const l={},h={};return Object.keys(r).forEach(d=>{const f=d.startsWith("fc")?h:l;f[d]=r[d]}),{featureExtractorMap:l,classifierMap:h}}const tD=Ye(Je());class fg extends kn{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return tD.tidy(()=>{const h=r instanceof po?this.faceFeatureExtractor.forwardInput(r):r;return zu(h.as2D(h.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:h}=this.extractClassifierParams(r);this._params=l,this._paramMappings=h}extractClassifierParams(r){return QE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:h}=mg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),eD(h)}extractParams(r){const l=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*l+h,f=r.slice(0,r.length-d),g=r.slice(r.length-d);return this.faceFeatureExtractor.extractWeights(f),this.extractClassifierParams(g)}}const ax=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class ya{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);ax.forEach((l,h)=>{this[l]=r[h]})}asSortedArray(){return ax.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Qc=Ye(Je());class cx extends fg{constructor(r=new pg){super("FaceExpressionNet",r)}forwardInput(r){return Qc.tidy(()=>Qc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Rt(r))}async predictExpressions(r){const l=await Rt(r),h=await this.forwardInput(l),d=await Promise.all(Qc.unstack(h).map(async g=>{const S=await g.data();return g.dispose(),S}));h.dispose();const f=d.map(g=>new ya(g));return l.isBatchInput?f:f[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function lx(r){return r.expressions instanceof ya}function gg(r,l){const h={expressions:l};return Object.assign({},r,h)}function kZ(r,l,h=.1,d){const f=Array.isArray(l)?l:[l];f.forEach(g=>{const S=g instanceof ya?g:lx(g)?g.expressions:void 0;if(!S)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const L=S.asSortedArray(),x=L.filter(C=>C.probability>h),A=Vi(g)?g.detection.box.bottomLeft:d||new Ze(0,0),O=new Ec(x.map(C=>`${C.expression} (${ea(C.probability)})`),A);O.draw(r)})}function ba(r){return Vi(r)&&r.landmarks instanceof qs&&r.unshiftedLandmarks instanceof qs&&r.alignedRect instanceof Yt}function el(r,l){const{box:h}=r.detection,d=l.shiftBy(h.x,h.y),f=d.align(),{imageDims:g}=r.detection,S=new Yt(r.detection.score,f.rescale(g.reverse()),g),L={landmarks:d,unshiftedLandmarks:l,alignedRect:S};return Object.assign({},r,L)}class nD{constructor(r={}){const{drawLines:l=!0,drawPoints:h=!0,lineWidth:d,lineColor:f,pointSize:g,pointColor:S}=r;this.drawLines=l,this.drawPoints=h,this.lineWidth=d||1,this.pointSize=g||2,this.lineColor=f||"rgba(0, 255, 255, 1)",this.pointColor=S||"rgba(255, 0, 255, 1)"}}class sD{constructor(r,l={}){this.faceLandmarks=r,this.options=new nD(l)}draw(r){const l=es(r),{drawLines:h,drawPoints:d,lineWidth:f,lineColor:g,pointSize:S,pointColor:L}=this.options;if(h&&this.faceLandmarks instanceof Iu&&(l.strokeStyle=g,l.lineWidth=f,gr(l,this.faceLandmarks.getJawOutline()),gr(l,this.faceLandmarks.getLeftEyeBrow()),gr(l,this.faceLandmarks.getRightEyeBrow()),gr(l,this.faceLandmarks.getNose()),gr(l,this.faceLandmarks.getLeftEye(),!0),gr(l,this.faceLandmarks.getRightEye(),!0),gr(l,this.faceLandmarks.getMouth(),!0)),d){l.strokeStyle=L,l.fillStyle=L;const x=A=>{l.beginPath(),l.arc(A.x,A.y,S,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function FZ(r,l){const h=Array.isArray(l)?l:[l];h.forEach(d=>{const f=d instanceof qs?d:ba(d)?d.landmarks:void 0;if(!f)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new sD(f).draw(r)})}const hx={};vc(hx,{AnchorPosition:()=>Hi,DrawBox:()=>ZS,DrawBoxOptions:()=>l2,DrawFaceLandmarks:()=>sD,DrawFaceLandmarksOptions:()=>nD,DrawTextField:()=>Ec,DrawTextFieldOptions:()=>Wm,drawContour:()=>gr,drawDetections:()=>GX,drawFaceExpressions:()=>kZ,drawFaceLandmarks:()=>FZ});function _Z(r,l){const h=Kc(r,l),d=Xc(r,l);function f(S,L,x){const A=d(S,L,`${x}/separable_conv0`),O=d(L,L,`${x}/separable_conv1`),C=h(S,L,1,`${x}/expansion_conv`);return{separable_conv0:A,separable_conv1:O,expansion_conv:C}}function g(S,L){const x=d(S,S,`${L}/separable_conv0`),A=d(S,S,`${L}/separable_conv1`),O=d(S,S,`${L}/separable_conv2`);return{separable_conv0:x,separable_conv1:A,separable_conv2:O}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:f,extractMainBlockParams:g}}function iD(r,l){const h=[],{extractWeights:d,getRemainingWeights:f}=Hn(r),{extractConvParams:g,extractSeparableConvParams:S,extractReductionBlockParams:L,extractMainBlockParams:x}=_Z(d,h),A=g(3,32,3,"entry_flow/conv_in"),O=L(32,64,"entry_flow/reduction_block_0"),C=L(64,128,"entry_flow/reduction_block_1"),$={conv_in:A,reduction_block_0:O,reduction_block_1:C},z={};zi(l,0,1).forEach(fe=>{z[`main_block_${fe}`]=x(128,`middle_flow/main_block_${fe}`)});const ne=L(128,256,"exit_flow/reduction_block"),te=S(256,512,"exit_flow/separable_conv"),se={reduction_block:ne,separable_conv:te};if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:h,params:{entry_flow:$,middle_flow:z,exit_flow:se}}}function WZ(r,l){const h=ys(r,l),d=ug(h),f=Jc(h);function g(L){const x=f(`${L}/separable_conv0`),A=f(`${L}/separable_conv1`),O=d(`${L}/expansion_conv`);return{separable_conv0:x,separable_conv1:A,expansion_conv:O}}function S(L){const x=f(`${L}/separable_conv0`),A=f(`${L}/separable_conv1`),O=f(`${L}/separable_conv2`);return{separable_conv0:x,separable_conv1:A,separable_conv2:O}}return{extractConvParams:d,extractSeparableConvParams:f,extractReductionBlockParams:g,extractMainBlockParams:S}}function rD(r,l){const h=[],{extractConvParams:d,extractSeparableConvParams:f,extractReductionBlockParams:g,extractMainBlockParams:S}=WZ(r,h),L=d("entry_flow/conv_in"),x=g("entry_flow/reduction_block_0"),A=g("entry_flow/reduction_block_1"),O={conv_in:L,reduction_block_0:x,reduction_block_1:A},C={};zi(l,0,1).forEach(te=>{C[`main_block_${te}`]=S(`middle_flow/main_block_${te}`)});const $=g("exit_flow/reduction_block"),z=f("exit_flow/separable_conv"),ne={reduction_block:$,separable_conv:z};return Vn(r,h),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:h}}const tn=Ye(Je());function oD(r,l,h){return tn.add(tn.conv2d(r,l.filters,h,"same"),l.bias)}function ux(r,l,h=!0){let d=h?tn.relu(r):r;return d=ns(d,l.separable_conv0,[1,1]),d=ns(tn.relu(d),l.separable_conv1,[1,1]),d=tn.maxPool(d,[3,3],[2,2],"same"),d=tn.add(d,oD(r,l.expansion_conv,[2,2])),d}function $Z(r,l){let h=ns(tn.relu(r),l.separable_conv0,[1,1]);return h=ns(tn.relu(h),l.separable_conv1,[1,1]),h=ns(tn.relu(h),l.separable_conv2,[1,1]),h=tn.add(h,r),h}class aD extends kn{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return tn.tidy(()=>{const h=tn.cast(r.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(tn.scalar(256));let g=tn.relu(oD(f,l.entry_flow.conv_in,[2,2]));return g=ux(g,l.entry_flow.reduction_block_0,!1),g=ux(g,l.entry_flow.reduction_block_1),zi(this._numMainBlocks,0,1).forEach(S=>{g=$Z(g,l.middle_flow[`main_block_${S}`])}),g=ux(g,l.exit_flow.reduction_block),g=tn.relu(ns(g,l.exit_flow.separable_conv,[1,1])),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return rD(r,this._numMainBlocks)}extractParams(r){return iD(r,this._numMainBlocks)}}function cD(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),f=lg(h,l),g=f(512,1,"fc/age"),S=f(512,2,"fc/gender");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{fc:{age:g,gender:S}}}}function lD(r){const l=[],h=ys(r,l);function d(g){const S=h(`${g}/weights`,2),L=h(`${g}/bias`,1);return{weights:S,bias:L}}const f={fc:{age:d("fc/age"),gender:d("fc/gender")}};return Vn(r,l),{params:f,paramMappings:l}}var Tr;(function(r){r.FEMALE="female",r.MALE="male"})(Tr||(Tr={}));const Xi=Ye(Je());class dx extends kn{constructor(r=new aD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Xi.tidy(()=>{const h=r instanceof po?this.faceFeatureExtractor.forwardInput(r):r,d=Xi.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),f=zu(d,l.fc.age).as1D(),g=zu(d,l.fc.gender);return{age:f,gender:g}})}forwardInput(r){return Xi.tidy(()=>{const{age:l,gender:h}=this.runNet(r);return{age:l,gender:Xi.softmax(h)}})}async forward(r){return this.forwardInput(await Rt(r))}async predictAgeAndGender(r){const l=await Rt(r),h=await this.forwardInput(l),d=Xi.unstack(h.age),f=Xi.unstack(h.gender),g=d.map((L,x)=>({ageTensor:L,genderTensor:f[x]})),S=await Promise.all(g.map(async({ageTensor:L,genderTensor:x})=>{const A=(await L.data())[0],O=(await x.data())[0],C=O>.5,$=C?Tr.MALE:Tr.FEMALE,z=C?O:1-O;return L.dispose(),x.dispose(),{age:A,gender:$,genderProbability:z}}));return h.age.dispose(),h.gender.dispose(),l.isBatchInput?S:S[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:h}=this.extractClassifierParams(r);this._params=l,this._paramMappings=h}extractClassifierParams(r){return cD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:h}=mg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),lD(h)}extractParams(r){const l=512*1+1+(512*2+2),h=r.slice(0,r.length-l),d=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}}const bs=Ye(Je());class yg extends fg{postProcess(r,l,h){const d=h.map(({width:g,height:S})=>{const L=l/Math.max(S,g);return{width:g*L,height:S*L}}),f=d.length;return bs.tidy(()=>{const g=(O,C)=>bs.stack([bs.fill([68],O,"float32"),bs.fill([68],C,"float32")],1).as2D(1,136).as1D(),S=(O,C)=>{const{width:$,height:z}=d[O];return C($,z)?Math.abs($-z)/2:0},L=O=>S(O,(C,$)=>C<$),x=O=>S(O,(C,$)=>$<C),A=r.mul(bs.fill([f,136],l,"float32")).sub(bs.stack(Array.from(Array(f),(O,C)=>g(L(C),x(C))))).div(bs.stack(Array.from(Array(f),(O,C)=>g(d[C].width,d[C].height))));return A})}forwardInput(r){return bs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(r){return this.forwardInput(await Rt(r))}async detectLandmarks(r){const l=await Rt(r),h=bs.tidy(()=>bs.unstack(this.forwardInput(l))),d=await Promise.all(h.map(async(f,g)=>{const S=Array.from(await f.data()),L=S.filter((A,O)=>Em(O)),x=S.filter((A,O)=>!Em(O));return new Iu(Array(68).fill(0).map((A,O)=>new Ze(L[O],x[O])),{height:l.getInputHeight(g),width:l.getInputWidth(g)})}));return h.forEach(f=>f.dispose()),l.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}}class Gu extends yg{constructor(r=new pg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function hD(r){const l=[],{extractDenseBlock3Params:h}=dg(r,l),d={dense0:h("dense0",!0),dense1:h("dense1"),dense2:h("dense2")};return Vn(r,l),{params:d,paramMappings:l}}function uD(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),{extractDenseBlock3Params:f}=hg(h,l),g=f(3,32,"dense0",!0),S=f(32,64,"dense1"),L=f(64,128,"dense2");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{dense0:g,dense1:S,dense2:L}}}const go=Ye(Je());class dD extends kn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return go.tidy(()=>{const h=go.cast(r.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(go.scalar(255));let g=og(f,l.dense0,!0);return g=og(g,l.dense1),g=og(g,l.dense2),g=go.avgPool(g,[14,14],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return hD(r)}extractParams(r){return uD(r)}}class px extends yg{constructor(r=new dD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class UZ extends Gu{}const bg=Ye(Je());function pD(r,l){return bg.add(bg.mul(r,l.weights),l.biases)}const tl=Ye(Je());function mx(r,l,h,d,f="same"){const{filters:g,bias:S}=l.conv;let L=tl.conv2d(r,g,h,f);return L=tl.add(L,S),L=pD(L,l.scale),d?tl.relu(L):L}function mD(r,l){return mx(r,l,[1,1],!0)}function fx(r,l){return mx(r,l,[1,1],!1)}function wg(r,l){return mx(r,l,[2,2],!0,"valid")}const ws=Ye(Je());function BZ(r,l){function h(L,x,A){const O=r(L),C=O.length/(x*A*A);if(MS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${A}`);return ws.tidy(()=>ws.transpose(ws.tensor4d(O,[x,C,A,A]),[2,3,1,0]))}function d(L,x,A,O){const C=h(L,x,A),$=ws.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:$}}function f(L,x){const A=ws.tensor1d(r(L)),O=ws.tensor1d(r(L));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:A,biases:O}}function g(L,x,A,O){const C=d(L,x,A,`${O}/conv`),$=f(x,`${O}/scale`);return{conv:C,scale:$}}function S(L,x,A,O,C=!1){const $=g((C?.5:1)*L,x,A,`${O}/conv1`),z=g(L,x,A,`${O}/conv2`);return{conv1:$,conv2:z}}return{extractConvLayerParams:g,extractResidualLayerParams:S}}function fD(r){const{extractWeights:l,getRemainingWeights:h}=Hn(r),d=[],{extractConvLayerParams:f,extractResidualLayerParams:g}=BZ(l,d),S=f(4704,32,7,"conv32_down"),L=g(9216,32,3,"conv32_1"),x=g(9216,32,3,"conv32_2"),A=g(9216,32,3,"conv32_3"),O=g(36864,64,3,"conv64_down",!0),C=g(36864,64,3,"conv64_1"),$=g(36864,64,3,"conv64_2"),z=g(36864,64,3,"conv64_3"),ne=g(147456,128,3,"conv128_down",!0),te=g(147456,128,3,"conv128_1"),se=g(147456,128,3,"conv128_2"),fe=g(589824,256,3,"conv256_down",!0),de=g(589824,256,3,"conv256_1"),Ae=g(589824,256,3,"conv256_2"),xe=g(589824,256,3,"conv256_down_out"),Me=ws.tidy(()=>ws.transpose(ws.tensor2d(l(256*128),[128,256]),[1,0]));if(d.push({paramPath:"fc"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);const Ke={conv32_down:S,conv32_1:L,conv32_2:x,conv32_3:A,conv64_down:O,conv64_1:C,conv64_2:$,conv64_3:z,conv128_down:ne,conv128_1:te,conv128_2:se,conv256_down:fe,conv256_1:de,conv256_2:Ae,conv256_down_out:xe,fc:Me};return{params:Ke,paramMappings:d}}function MZ(r,l){const h=ys(r,l);function d(S){const L=h(`${S}/scale/weights`,1),x=h(`${S}/scale/biases`,1);return{weights:L,biases:x}}function f(S){const L=h(`${S}/conv/filters`,4),x=h(`${S}/conv/bias`,1),A=d(S);return{conv:{filters:L,bias:x},scale:A}}function g(S){return{conv1:f(`${S}/conv1`),conv2:f(`${S}/conv2`)}}return{extractConvLayerParams:f,extractResidualLayerParams:g}}function gD(r){const l=[],{extractConvLayerParams:h,extractResidualLayerParams:d}=MZ(r,l),f=h("conv32_down"),g=d("conv32_1"),S=d("conv32_2"),L=d("conv32_3"),x=d("conv64_down"),A=d("conv64_1"),O=d("conv64_2"),C=d("conv64_3"),$=d("conv128_down"),z=d("conv128_1"),ne=d("conv128_2"),te=d("conv256_down"),se=d("conv256_1"),fe=d("conv256_2"),de=d("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!BS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const xe={conv32_down:f,conv32_1:g,conv32_2:S,conv32_3:L,conv64_down:x,conv64_1:A,conv64_2:O,conv64_3:C,conv128_down:$,conv128_1:z,conv128_2:ne,conv256_down:te,conv256_1:se,conv256_2:fe,conv256_down_out:de,fc:Ae};return Vn(r,l),{params:xe,paramMappings:l}}const Yn=Ye(Je());function Li(r,l){let h=mD(r,l.conv1);return h=fx(h,l.conv2),h=Yn.add(h,r),h=Yn.relu(h),h}function Vu(r,l){let h=wg(r,l.conv1);h=fx(h,l.conv2);let d=Yn.avgPool(r,2,2,"valid");const f=Yn.zeros(d.shape),g=d.shape[3]!==h.shape[3],S=d.shape[1]!==h.shape[1]||d.shape[2]!==h.shape[2];if(S){const L=[...h.shape];L[1]=1;const x=Yn.zeros(L);h=Yn.concat([h,x],1);const A=[...h.shape];A[2]=1;const O=Yn.zeros(A);h=Yn.concat([h,O],2)}return d=g?Yn.concat([d,f],3):d,h=Yn.add(d,h),h=Yn.relu(h),h}const _s=Ye(Je());class Hu extends kn{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return _s.tidy(()=>{const h=_s.cast(r.toBatchTensor(150,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(_s.scalar(256));let g=wg(f,l.conv32_down);g=_s.maxPool(g,3,2,"valid"),g=Li(g,l.conv32_1),g=Li(g,l.conv32_2),g=Li(g,l.conv32_3),g=Vu(g,l.conv64_down),g=Li(g,l.conv64_1),g=Li(g,l.conv64_2),g=Li(g,l.conv64_3),g=Vu(g,l.conv128_down),g=Li(g,l.conv128_1),g=Li(g,l.conv128_2),g=Vu(g,l.conv256_down),g=Li(g,l.conv256_1),g=Li(g,l.conv256_2),g=Vu(g,l.conv256_down_out);const S=g.mean([1,2]),L=_s.matMul(S,l.fc);return L})}async forward(r){return this.forwardInput(await Rt(r))}async computeFaceDescriptor(r){const l=await Rt(r),h=_s.tidy(()=>_s.unstack(this.forwardInput(l))),d=await Promise.all(h.map(f=>f.data()));return h.forEach(f=>f.dispose()),l.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return gD(r)}extractParams(r){return fD(r)}}function PZ(r){const l=new Hu;return l.extractWeights(r),l}function Lg(r,l){const h={descriptor:l};return Object.assign({},r,h)}function zZ(r){return typeof r.age=="number"}function Sg(r,l){const h={age:l};return Object.assign({},r,h)}function GZ(r){return(r.gender===Tr.MALE||r.gender===Tr.FEMALE)&&Rc(r.genderProbability)}function Ig(r,l,h){const d={gender:l,genderProbability:h};return Object.assign({},r,d)}const Si=Ye(Je());function VZ(r,l){function h(x,A){const 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x=f(1024,256,1,"prediction_layer/conv_0"),A=f(256,512,3,"prediction_layer/conv_1"),O=f(512,128,1,"prediction_layer/conv_2"),C=f(128,256,3,"prediction_layer/conv_3"),$=f(256,128,1,"prediction_layer/conv_4"),z=f(128,256,3,"prediction_layer/conv_5"),ne=f(256,64,1,"prediction_layer/conv_6"),te=f(64,128,3,"prediction_layer/conv_7"),se=d(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),fe=d(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),de=d(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=d(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),xe=d(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Me=d(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),Ke=d(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),wt=d(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=d(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=d(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Fn=d(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),vn=d(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),Nn={box_encoding_predictor:se,class_predictor:fe},Qs={box_encoding_predictor:de,class_predictor:Ae},Ai={box_encoding_predictor:xe,class_predictor:Me},ei={box_encoding_predictor:Ke,class_predictor:wt},xa={box_encoding_predictor:$t,class_predictor:Kt},hl={box_encoding_predictor:Fn,class_predictor:vn};return{conv_0:x,conv_1:A,conv_2:O,conv_3:C,conv_4:$,conv_5:z,conv_6:ne,conv_7:te,box_predictor_0:Nn,box_predictor_1:Qs,box_predictor_2:Ai,box_predictor_3:ei,box_predictor_4:xa,box_predictor_5:hl}}return{extractMobilenetV1Params:S,extractPredictionLayerParams:L}}function 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f=[l.conv_1,l.conv_2,l.conv_3,l.conv_4,l.conv_5,l.conv_6,l.conv_7,l.conv_8,l.conv_9,l.conv_10,l.conv_11,l.conv_12,l.conv_13];if(f.forEach((g,S)=>{const L=S+1,x=jZ(L);d=qZ(d,g.depthwise_conv,x),d=Xs(d,g.pointwise_conv,[1,1]),L===11&&(h=d)}),h===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:d,conv11:h}})}function LD(r,l,h,d,f){const g=r.shape[0],S=Math.min(h,g),L=l.map((O,C)=>({score:O,boxIndex:C})).filter(O=>O.score>f).sort((O,C)=>C.score-O.score),x=O=>O<=d?1:0,A=[];return L.forEach(O=>{if(A.length>=S)return;const C=O.score;for(let $=A.length-1;$>=0;--$){const z=KZ(r,O.boxIndex,A[$]);if(z===0)continue;if(O.score*=x(z),O.score<=f)break}C===O.score&&A.push(O.boxIndex)}),A}function KZ(r,l,h){const d=r.arraySync(),f=Math.min(d[l][0],d[l][2]),g=Math.min(d[l][1],d[l][3]),S=Math.max(d[l][0],d[l][2]),L=Math.max(d[l][1],d[l][3]),x=Math.min(d[h][0],d[h][2]),A=Math.min(d[h][1],d[h][3]),O=Math.max(d[h][0],d[h][2]),C=Math.max(d[h][1],d[h][3]),$=(S-f)*(L-g),z=(O-x)*(C-A);if($<=0||z<=0)return 0;const ne=Math.max(f,x),te=Math.max(g,A),se=Math.min(S,O),fe=Math.min(L,C),de=Math.max(se-ne,0)*Math.max(fe-te,0);return de/($+z-de)}const De=Ye(Je());function XZ(r){const l=De.unstack(De.transpose(r,[1,0])),h=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],d=[De.add(l[0],De.div(h[0],De.scalar(2))),De.add(l[1],De.div(h[1],De.scalar(2)))];return{sizes:h,centers:d}}function JZ(r,l){const{sizes:h,centers:d}=XZ(r),f=De.unstack(De.transpose(l,[1,0])),g=De.div(De.mul(De.exp(De.div(f[2],De.scalar(5))),h[0]),De.scalar(2)),S=De.add(De.mul(De.div(f[0],De.scalar(10)),h[0]),d[0]),L=De.div(De.mul(De.exp(De.div(f[3],De.scalar(5))),h[1]),De.scalar(2)),x=De.add(De.mul(De.div(f[1],De.scalar(10)),h[1]),d[1]);return De.transpose(De.stack([De.sub(S,g),De.sub(x,L),De.add(S,g),De.add(x,L)]),[1,0])}function SD(r,l,h){return De.tidy(()=>{const d=r.shape[0];let f=JZ(De.reshape(De.tile(h.extra_dim,[d,1,1]),[-1,4]),De.reshape(r,[-1,4]));f=De.reshape(f,[d,f.shape[0]/d,4]);const g=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let S=De.slice(g,[0,0,0],[-1,-1,1]);S=De.reshape(S,[d,S.shape[1]]);const L=De.unstack(f),x=De.unstack(S);return{boxes:L,scores:x}})}const Yu=Ye(Je());function wa(r,l){return Yu.tidy(()=>{const h=r.shape[0],d=Yu.reshape(ga(r,l.box_encoding_predictor),[h,-1,1,4]),f=Yu.reshape(ga(r,l.class_predictor),[h,-1,3]);return{boxPredictionEncoding:d,classPrediction:f}})}const qu=Ye(Je());function ID(r,l,h){return qu.tidy(()=>{const d=Xs(r,h.conv_0,[1,1]),f=Xs(d,h.conv_1,[2,2]),g=Xs(f,h.conv_2,[1,1]),S=Xs(g,h.conv_3,[2,2]),L=Xs(S,h.conv_4,[1,1]),x=Xs(L,h.conv_5,[2,2]),A=Xs(x,h.conv_6,[1,1]),O=Xs(A,h.conv_7,[2,2]),C=wa(l,h.box_predictor_0),$=wa(r,h.box_predictor_1),z=wa(f,h.box_predictor_2),ne=wa(S,h.box_predictor_3),te=wa(x,h.box_predictor_4),se=wa(O,h.box_predictor_5),fe=qu.concat([C.boxPredictionEncoding,$.boxPredictionEncoding,z.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,se.boxPredictionEncoding],1),de=qu.concat([C.classPrediction,$.classPrediction,z.classPrediction,ne.classPrediction,te.classPrediction,se.classPrediction],1);return{boxPredictions:fe,classPredictions:de}})}class Ii{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const xi=Ye(Je());class nl extends kn{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return xi.tidy(()=>{const h=xi.cast(r.toBatchTensor(512,!1),"float32"),d=xi.sub(xi.mul(h,xi.scalar(.007843137718737125)),xi.scalar(1)),f=wD(d,l.mobilenetv1),{boxPredictions:g,classPredictions:S}=ID(f.out,f.conv11,l.prediction_layer);return SD(g,S,l.output_layer)})}async forward(r){return this.forwardInput(await Rt(r))}async locateFaces(r,l={}){const{maxResults:h,minConfidence:d}=new Ii(l),f=await Rt(r),{boxes:g,scores:S}=this.forwardInput(f),L=g[0],x=S[0];for(let de=1;de<g.length;de++)g[de].dispose(),S[de].dispose();const A=Array.from(await x.data()),O=.5,C=LD(L,A,h,O,d),$=f.getReshapedInputDimensions(0),z=f.inputSize,ne=z/$.width,te=z/$.height,se=L.arraySync(),fe=C.map(de=>{const[Ae,xe]=[Math.max(0,se[de][0]),Math.min(1,se[de][2])].map(wt=>wt*te),[Me,Ke]=[Math.max(0,se[de][1]),Math.min(1,se[de][3])].map(wt=>wt*ne);return new Yt(A[de],new Su(Me,Ae,Ke-Me,xe-Ae),{height:f.getInputHeight(0),width:f.getInputWidth(0)})});return L.dispose(),x.dispose(),fe}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return bD(r)}extractParams(r){return yD(r)}}function xD(r){const l=new nl;return l.extractWeights(r),l}function ZZ(r){return xD(r)}class QZ extends nl{}const TD=.4,AD=[new Ze(.738768,.874946),new Ze(2.42204,2.65704),new Ze(4.30971,7.04493),new Ze(10.246,4.59428),new Ze(12.6868,11.8741)],vD=[new Ze(1.603231,2.094468),new Ze(6.041143,7.080126),new Ze(2.882459,3.518061),new Ze(4.266906,5.178857),new Ze(9.041765,10.66308)],ND=[117.001,114.697,97.404],CD="tiny_yolov2_model",RD="tiny_yolov2_separable_conv_model";const xg=r=>typeof r=="number";function gx(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!xg(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>xg(l.x)&&xg(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(xg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Js=Ye(Je());function sl(r){return Js.tidy(()=>{const l=Js.mul(r,Js.scalar(.10000000149011612));return Js.add(Js.relu(Js.sub(r,l)),l)})}const Zs=Ye(Je());function vr(r,l){return Zs.tidy(()=>{let h=Zs.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return h=Zs.conv2d(h,l.conv.filters,[1,1],"valid"),h=Zs.sub(h,l.bn.sub),h=Zs.mul(h,l.bn.truediv),h=Zs.add(h,l.conv.bias),sl(h)})}const bo=Ye(Je());function Nr(r,l){return bo.tidy(()=>{let h=bo.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return h=bo.separableConv2d(h,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),h=bo.add(h,l.bias),sl(h)})}const yx=Ye(Je());function eQ(r,l){const h=Kc(r,l);function d(S,L){const x=yx.tensor1d(r(S)),A=yx.tensor1d(r(S));return l.push({paramPath:`${L}/sub`},{paramPath:`${L}/truediv`}),{sub:x,truediv:A}}function f(S,L,x){const A=h(S,L,3,`${x}/conv`),O=d(L,`${x}/bn`);return{conv:A,bn:O}}const g=Xc(r,l);return{extractConvParams:h,extractConvWithBatchNormParams:f,extractSeparableConvParams:g}}function OD(r,l,h,d){const{extractWeights:f,getRemainingWeights:g}=Hn(r),S=[],{extractConvParams:L,extractConvWithBatchNormParams:x,extractSeparableConvParams:A}=eQ(f,S);let O;if(l.withSeparableConvs){const[C,$,z,ne,te,se,fe,de,Ae]=d,xe=l.isFirstLayerConv2d?L(C,$,3,"conv0"):A(C,$,"conv0"),Me=A($,z,"conv1"),Ke=A(z,ne,"conv2"),wt=A(ne,te,"conv3"),$t=A(te,se,"conv4"),Kt=A(se,fe,"conv5"),Fn=de?A(fe,de,"conv6"):void 0,vn=Ae?A(de,Ae,"conv7"):void 0,Nn=L(Ae||de||fe,5*h,1,"conv8");O={conv0:xe,conv1:Me,conv2:Ke,conv3:wt,conv4:$t,conv5:Kt,conv6:Fn,conv7:vn,conv8:Nn}}else{const[C,$,z,ne,te,se,fe,de,Ae]=d,xe=x(C,$,"conv0"),Me=x($,z,"conv1"),Ke=x(z,ne,"conv2"),wt=x(ne,te,"conv3"),$t=x(te,se,"conv4"),Kt=x(se,fe,"conv5"),Fn=x(fe,de,"conv6"),vn=x(de,Ae,"conv7"),Nn=L(Ae,5*h,1,"conv8");O={conv0:xe,conv1:Me,conv2:Ke,conv3:wt,conv4:$t,conv5:Kt,conv6:Fn,conv7:vn,conv8:Nn}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:O,paramMappings:S}}function tQ(r,l){const h=ys(r,l);function d(L){const x=h(`${L}/sub`,1),A=h(`${L}/truediv`,1);return{sub:x,truediv:A}}function f(L){const x=h(`${L}/filters`,4),A=h(`${L}/bias`,1);return{filters:x,bias:A}}function g(L){const x=f(`${L}/conv`),A=d(`${L}/bn`);return{conv:x,bn:A}}const S=Jc(h);return{extractConvParams:f,extractConvWithBatchNormParams:g,extractSeparableConvParams:S}}function ED(r,l){const h=[],{extractConvParams:d,extractConvWithBatchNormParams:f,extractSeparableConvParams:g}=tQ(r,h);let S;if(l.withSeparableConvs){const L=l.filterSizes&&l.filterSizes.length||9;S={conv0:l.isFirstLayerConv2d?d("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:L>7?g("conv6"):void 0,conv7:L>8?g("conv7"):void 0,conv8:d("conv8")}}else S={conv0:f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:f("conv6"),conv7:f("conv7"),conv8:d("conv8")};return Vn(r,h),{params:S,paramMappings:h}}var bx;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(bx||(bx={}));class Cr{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Ye(Je());class il extends kn{constructor(r){super("TinyYolov2");gx(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let h=vr(r,l.conv0);return h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv1),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv2),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv3),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv4),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv5),h=kt.maxPool(h,[2,2],[1,1],"same"),h=vr(h,l.conv6),h=vr(h,l.conv7),ga(h,l.conv8,"valid",!1)}runMobilenet(r,l){let h=this.config.isFirstLayerConv2d?sl(ga(r,l.conv0,"valid",!1)):Nr(r,l.conv0);return h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv1),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv2),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv3),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv4),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv5),h=kt.maxPool(h,[2,2],[1,1],"same"),h=l.conv6?Nr(h,l.conv6):h,h=l.conv7?Nr(h,l.conv7):h,ga(h,l.conv8,"valid",!1)}forwardInput(r,l){const{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let d=kt.cast(r.toBatchTensor(l,!1),"float32");return d=this.config.meanRgb?yi(d,this.config.meanRgb):d,d=d.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(r,l){return await this.forwardInput(await Rt(r),l)}async detect(r,l={}){const{inputSize:h,scoreThreshold:d}=new Cr(l),f=await Rt(r),g=await this.forwardInput(f,h),S=kt.tidy(()=>kt.unstack(g)[0].expandDims()),L={width:f.getInputWidth(0),height:f.getInputHeight(0)},x=await this.extractBoxes(S,f.getReshapedInputDimensions(0),d);g.dispose(),S.dispose();const A=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),$=x.map(te=>this.config.classes[te.label]),z=VS(A.map(te=>te.rescale(h)),O,this.config.iouThreshold,!0),ne=z.map(te=>new Oc(O[te],C[te],$[te],A[te],L));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return ED(r,this.config)}extractParams(r){const l=this.config.filterSizes||il.DEFAULT_FILTER_SIZES,h=l?l.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return OD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,h){const{width:d,height:f}=l,g=Math.max(d,f),S=g/d,L=g/f,x=r.shape[1],A=this.config.anchors.length,[O,C,$]=kt.tidy(()=>{const se=r.reshape([x,x,A,this.boxEncodingSize]),fe=se.slice([0,0,0,0],[x,x,A,4]),de=se.slice([0,0,0,4],[x,x,A,1]),Ae=this.withClassScores?kt.softmax(se.slice([0,0,0,5],[x,x,A,this.config.classes.length]),3):kt.scalar(0);return[fe,de,Ae]}),z=[],ne=await C.array(),te=await O.array();for(let se=0;se<x;se++)for(let fe=0;fe<x;fe++)for(let de=0;de<A;de++){const Ae=Lu(ne[se][fe][de][0]);if(!h||Ae>h){const xe=(fe+Lu(te[se][fe][de][0]))/x*S,Me=(se+Lu(te[se][fe][de][1]))/x*L,Ke=Math.exp(te[se][fe][de][2])*this.config.anchors[de].x/x*S,wt=Math.exp(te[se][fe][de][3])*this.config.anchors[de].y/x*L,$t=xe-Ke/2,Kt=Me-wt/2,Fn={row:se,col:fe,anchor:de},{classScore:vn,label:Nn}=this.withClassScores?await this.extractPredictedClass($,Fn):{classScore:1,label:0};z.push({box:new wu($t,Kt,$t+Ke,Kt+wt),score:Ae,classScore:Ae*vn,label:Nn,...Fn})}}return O.dispose(),C.dispose(),$.dispose(),z}async extractPredictedClass(r,l){const{row:h,col:d,anchor:f}=l,g=await r.array();return Array(this.config.classes.length).fill(0).map((S,L)=>g[h][d][f][L]).map((S,L)=>({classScore:S,label:L})).reduce((S,L)=>S.classScore>L.classScore?S:L)}}il.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class ju extends il{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:TD,classes:["face"]},r?{anchors:vD,meanRgb:ND}:{anchors:AD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const h=await this.detect(r,l);return h.map(d=>new Yt(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?RD:CD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function nQ(r,l=!0){const h=new ju(l);return h.extractWeights(r),h}class wx extends Cr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Ti{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const Lx=Ye(Je());async function La(r,l,h,d,f=({alignedRect:g})=>g){const g=r.map(x=>ba(x)?f(x):x.detection),S=d||(l instanceof Lx.Tensor?await qc(l,g):await Yc(l,g)),L=await h(S);return S.forEach(x=>x instanceof Lx.Tensor&&x.dispose()),L}async function rl(r,l,h,d,f){return La([r],l,async g=>h(g[0]),d,f)}const DD=.4,kD=[new Ze(1.603231,2.094468),new Ze(6.041143,7.080126),new Ze(2.882459,3.518061),new Ze(4.266906,5.178857),new Ze(9.041765,10.66308)],FD=[117.001,114.697,97.404];class Ku extends il{constructor(){const r={withSeparableConvs:!0,iouThreshold:DD,classes:["face"],anchors:kD,meanRgb:FD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const h=await this.detect(r,l);return h.map(d=>new Yt(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const pt={ssdMobilenetv1:new nl,tinyFaceDetector:new Ku,tinyYolov2:new ju,faceLandmark68Net:new Gu,faceLandmark68TinyNet:new px,faceRecognitionNet:new Hu,faceExpressionNet:new cx,ageGenderNet:new dx},_D=(r,l)=>pt.ssdMobilenetv1.locateFaces(r,l),sQ=(r,l)=>pt.tinyFaceDetector.locateFaces(r,l),iQ=(r,l)=>pt.tinyYolov2.locateFaces(r,l),WD=r=>pt.faceLandmark68Net.detectLandmarks(r),rQ=r=>pt.faceLandmark68TinyNet.detectLandmarks(r),oQ=r=>pt.faceRecognitionNet.computeFaceDescriptor(r),aQ=r=>pt.faceExpressionNet.predictExpressions(r),cQ=r=>pt.ageGenderNet.predictAgeAndGender(r),$D=r=>pt.ssdMobilenetv1.load(r),lQ=r=>pt.tinyFaceDetector.load(r),hQ=r=>pt.tinyYolov2.load(r),uQ=r=>pt.faceLandmark68Net.load(r),dQ=r=>pt.faceLandmark68TinyNet.load(r),pQ=r=>pt.faceRecognitionNet.load(r),mQ=r=>pt.faceExpressionNet.load(r),fQ=r=>pt.ageGenderNet.load(r),gQ=$D,yQ=_D,bQ=WD;class UD extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.extractedFaces=h}}class Zu extends UD{async run(){const r=await this.parentTask,l=await La(r,this.input,async h=>await Promise.all(h.map(d=>pt.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return r.map((h,d)=>gg(h,l[d]))}withAgeAndGender(){return new Xu(this,this.input)}}class Qu extends UD{async run(){const r=await this.parentTask;if(!r)return;const l=await rl(r,this.input,h=>pt.faceExpressionNet.predictExpressions(h),this.extractedFaces);return gg(r,l)}withAgeAndGender(){return new Ju(this,this.input)}}class cl extends Zu{withAgeAndGender(){return new ol(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}}class ll extends Qu{withAgeAndGender(){return new al(this,this.input)}withFaceDescriptor(){return new Ia(this,this.input)}}class BD extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.extractedFaces=h}}class Xu extends BD{async run(){const r=await this.parentTask,l=await La(r,this.input,async h=>await Promise.all(h.map(d=>pt.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return r.map((h,d)=>{const{age:f,gender:g,genderProbability:S}=l[d];return Sg(Ig(h,g,S),f)})}withFaceExpressions(){return new Zu(this,this.input)}}class Ju extends BD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:h,genderProbability:d}=await rl(r,this.input,f=>pt.ageGenderNet.predictAgeAndGender(f),this.extractedFaces);return Sg(Ig(r,h,d),l)}withFaceExpressions(){return new Qu(this,this.input)}}class ol extends Xu{withFaceExpressions(){return new cl(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}}class al extends Ju{withFaceExpressions(){return new ll(this,this.input)}withFaceDescriptor(){return new Ia(this,this.input)}}class Sx extends Ti{constructor(r,l){super();this.parentTask=r;this.input=l}}class Sa extends Sx{async run(){const r=await this.parentTask,l=await La(r,this.input,h=>Promise.all(h.map(d=>pt.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return l.map((h,d)=>Lg(r[d],h))}withFaceExpressions(){return new cl(this,this.input)}withAgeAndGender(){return new ol(this,this.input)}}class Ia extends Sx{async run(){const r=await this.parentTask;if(!r)return;const l=await rl(r,this.input,h=>pt.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return Lg(r,l)}withFaceExpressions(){return new ll(this,this.input)}withAgeAndGender(){return new al(this,this.input)}}const ed=Ye(Je());class Ix extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?pt.faceLandmark68TinyNet:pt.faceLandmark68Net}}class xx extends Ix{async run(){const r=await this.parentTask,l=r.map(f=>f.detection),h=this.input instanceof ed.Tensor?await qc(this.input,l):await Yc(this.input,l),d=await Promise.all(h.map(f=>this.landmarkNet.detectLandmarks(f)));return h.forEach(f=>f instanceof ed.Tensor&&f.dispose()),r.map((f,g)=>el(f,d[g]))}withFaceExpressions(){return new cl(this,this.input)}withAgeAndGender(){return new ol(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}}class Tx extends Ix{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,h=this.input instanceof ed.Tensor?await qc(this.input,[l]):await Yc(this.input,[l]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(f=>f instanceof ed.Tensor&&f.dispose()),el(r,d)}withFaceExpressions(){return new ll(this,this.input)}withAgeAndGender(){return new al(this,this.input)}withFaceDescriptor(){return new Ia(this,this.input)}}class Ax extends Ti{constructor(r,l=new Ii){super();this.input=r;this.options=l}}class Tg extends Ax{async run(){const{input:r,options:l}=this,h=l instanceof wx?d=>pt.tinyFaceDetector.locateFaces(d,l):l instanceof Ii?d=>pt.ssdMobilenetv1.locateFaces(d,l):l instanceof Cr?d=>pt.tinyYolov2.locateFaces(d,l):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(h=>sa({},h)))})}withFaceLandmarks(r=!1){return new xx(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Xu(this.runAndExtendWithFaceDetections(),this.input)}}class vx extends Ax{async run(){const r=await new Tg(this.input,this.options);let l=r[0];return r.forEach(h=>{h.score>l.score&&(l=h)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?sa({},l):void 0)})}withFaceLandmarks(r=!1){return new Tx(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Qu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ju(this.runAndExtendWithFaceDetection(),this.input)}}function wQ(r,l=new Ii){return new vx(r,l)}function Ag(r,l=new Ii){return new Tg(r,l)}async function MD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Ag(r,new Ii(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function LQ(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Ag(r,new Cr(l)).withFaceLandmarks().withFaceDescriptors()}const SQ=MD;function Nx(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const h=Array.from(r),d=Array.from(l);return Math.sqrt(h.map((f,g)=>f-d[g]).reduce((f,g)=>f+Math.pow(g,2),0))}class PD{constructor(r,l=.6){this._distanceThreshold=l;const h=Array.isArray(r)?r:[r];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1;const f=()=>`person ${d++}`;this._labeledDescriptors=h.map(g=>{if(g instanceof na)return g;if(g instanceof Float32Array)return new na(f(),[g]);if(g.descriptor&&g.descriptor instanceof Float32Array)return new na(f(),[g.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(h=>Nx(h,r)).reduce((h,d)=>h+d,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:h})=>new km(h,this.computeMeanDistance(r,l))).reduce((l,h)=>l.distance<h.distance?l:h)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new km("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(h=>na.fromJSON(h));return new PD(l,r.distanceThreshold)}}function IQ(r){const l=new Ku;return l.extractWeights(r),l}function zD(r,l){const{width:h,height:d}=new ms(l.width,l.height);if(h<=0||d<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:h,height:d})}`);if(Array.isArray(r))return r.map(f=>zD(f,{width:h,height:d}));if(ba(r)){const f=r.detection.forSize(h,d),g=r.unshiftedLandmarks.forSize(f.box.width,f.box.height);return el(sa(r,f),g)}return Vi(r)?sa(r,r.detection.forSize(h,d)):r instanceof qs||r instanceof Yt?r.forSize(h,d):r}var GD="0.8.3";vc(exports,{AgeGenderNet:()=>dx,BoundingBox:()=>wu,Box:()=>Ct,ComposableTask:()=>Ti,ComputeAllFaceDescriptorsTask:()=>Sa,ComputeFaceDescriptorsTaskBase:()=>Sx,ComputeSingleFaceDescriptorTask:()=>Ia,DetectAllFaceLandmarksTask:()=>xx,DetectAllFacesTask:()=>Tg,DetectFaceLandmarksTaskBase:()=>Ix,DetectFacesTaskBase:()=>Ax,DetectSingleFaceLandmarksTask:()=>Tx,DetectSingleFaceTask:()=>vx,Dimensions:()=>ms,FACE_EXPRESSION_LABELS:()=>ax,FaceDetection:()=>Yt,FaceDetectionNet:()=>QZ,FaceExpressionNet:()=>cx,FaceExpressions:()=>ya,FaceLandmark68Net:()=>Gu,FaceLandmark68TinyNet:()=>px,FaceLandmarkNet:()=>UZ,FaceLandmarks:()=>qs,FaceLandmarks5:()=>BX,FaceLandmarks68:()=>Iu,FaceMatch:()=>km,FaceMatcher:()=>PD,FaceRecognitionNet:()=>Hu,Gender:()=>Tr,LabeledBox:()=>Fm,LabeledFaceDescriptors:()=>na,NetInput:()=>po,NeuralNetwork:()=>kn,ObjectDetection:()=>Oc,Point:()=>Ze,PredictedBox:()=>MX,Rect:()=>Su,SsdMobilenetv1:()=>nl,SsdMobilenetv1Options:()=>Ii,TinyFaceDetector:()=>Ku,TinyFaceDetectorOptions:()=>wx,TinyYolov2:()=>ju,TinyYolov2Options:()=>Cr,TinyYolov2SizeType:()=>bx,allFaces:()=>SQ,allFacesSsdMobilenetv1:()=>MD,allFacesTinyYolov2:()=>LQ,awaitMediaLoaded:()=>QS,bufferToImage:()=>eI,computeFaceDescriptor:()=>oQ,createCanvas:()=>Dc,createCanvasFromMedia:()=>Tu,createFaceDetectionNet:()=>ZZ,createFaceRecognitionNet:()=>PZ,createSsdMobilenetv1:()=>xD,createTinyFaceDetector:()=>IQ,createTinyYolov2:()=>nQ,detectAllFaces:()=>Ag,detectFaceLandmarks:()=>WD,detectFaceLandmarksTiny:()=>rQ,detectLandmarks:()=>bQ,detectSingleFace:()=>wQ,draw:()=>hx,env:()=>gt,euclideanDistance:()=>Nx,extendWithAge:()=>Sg,extendWithFaceDescriptor:()=>Lg,extendWithFaceDetection:()=>sa,extendWithFaceExpressions:()=>gg,extendWithFaceLandmarks:()=>el,extendWithGender:()=>Ig,extractFaceTensors:()=>qc,extractFaces:()=>Yc,fetchImage:()=>OZ,fetchJson:()=>ix,fetchNetWeights:()=>EZ,fetchOrThrow:()=>fa,getContext2dOrThrow:()=>es,getMediaDimensions:()=>ra,imageTensorToCanvas:()=>tI,imageToSquare:()=>sx,inverseSigmoid:()=>_X,iou:()=>zS,isMediaElement:()=>Um,isMediaLoaded:()=>xu,isWithAge:()=>zZ,isWithFaceDetection:()=>Vi,isWithFaceExpressions:()=>lx,isWithFaceLandmarks:()=>ba,isWithGender:()=>GZ,loadAgeGenderModel:()=>fQ,loadFaceDetectionModel:()=>gQ,loadFaceExpressionModel:()=>mQ,loadFaceLandmarkModel:()=>uQ,loadFaceLandmarkTinyModel:()=>dQ,loadFaceRecognitionModel:()=>pQ,loadSsdMobilenetv1Model:()=>$D,loadTinyFaceDetectorModel:()=>lQ,loadTinyYolov2Model:()=>hQ,loadWeightMap:()=>rx,locateFaces:()=>yQ,matchDimensions:()=>DZ,minBbox:()=>GS,nets:()=>pt,nonMaxSuppression:()=>VS,normalize:()=>yi,padToSquare:()=>HS,predictAgeAndGender:()=>cQ,recognizeFaceExpressions:()=>aQ,resizeResults:()=>zD,resolveInput:()=>ia,shuffleArray:()=>FX,sigmoid:()=>Lu,ssdMobilenetv1:()=>_D,tf:()=>xQ,tinyFaceDetector:()=>sQ,tinyYolov2:()=>iQ,toNetInput:()=>Rt,utils:()=>US,validateConfig:()=>gx,version:()=>vQ});const xQ=Ye(Je()),TQ=typeof process!="undefined",AQ=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",vQ={faceapi:GD,node:TQ,browser:AQ};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=face-api.node.js.map
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