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

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var faceapi=(()=>{var Xm=Object.defineProperty,s9=Object.prototype.hasOwnProperty,Jm=(r,l)=>()=>(l||(l={exports:{}},r(l.exports,l)),l.exports),s2=r=>Xm(r,"__esModule",{value:!0}),vu=(r,l)=>{s2(r);for(var u in l)Xm(r,u,{get:l[u],enumerable:!0})},i9=(r,l)=>{if(s2(r),typeof l=="object"||typeof l=="function")for(let u in l)!s9.call(r,u)&&u!=="default"&&Xm(r,u,{get:()=>l[u],enumerable:!0});return r},Ze=r=>r&&r.__esModule?r:i9(Xm({},"default",{value:r,enumerable:!0}),r);var r2=Jm((Ec,i2)=>{"use strict";var r9=function(){if(typeof self!="undefined")return self;if(typeof window!="undefined")return window;if(typeof gr!="undefined")return gr;throw new Error("unable to locate global object")},gr=r9();i2.exports=Ec=gr.fetch;gr.fetch&&(Ec.default=gr.fetch.bind(gr));Ec.Headers=gr.Headers;Ec.Request=gr.Request;Ec.Response=gr.Response});var Qe=Jm((Zm,o2)=>{(function(r,l){typeof Zm=="object"&&typeof o2!="undefined"?l(Zm):typeof 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t==="string"?Hd(e):Er([e],t)}function Fk(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function Er(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=te(e)),oe().getBool("DEBUG")&&Cr(e,t),Fk(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){const n=new Uint8Array(e.length);for(let s=0;s<n.length;++s)Math.round(e[s])!==0&&(n[s]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Kn(){return oe().platform.now()}function lT(e,t){return oe().platform.fetch(e,t)}function Hd(e,t="utf-8"){return t=t||"utf-8",oe().platform.encode(e,t)}function rh(e,t="utf-8"){return t=t||"utf-8",oe().platform.decode(e,t)}var _k=Object.freeze({__proto__:null,createScalarValue:cT,toTypedArray:Er,now:Kn,fetch:lT,encodeString:Hd,decodeString:rh,shuffle:I,clamp:S,nearestLargerEven:T,sum:C,randUniform:D,distSquared:_,assert:A,assertShapesMatch:B,assertNonNull:ne,flatten:te,sizeFromShape:P,isScalarShape:ge,arraysEqual:ae,isInt:Le,tanh:ve,sizeToSquarishShape:Ve,createShuffledIndices:at,rightPad:pt,repeatedTry:$t,inferFromImplicitShape:Gt,parseAxisParam:qe,squeezeShape:ln,getTypedArrayFromDType:bt,getArrayFromDType:ws,checkConversionForErrors:Cr,isValidDtype:Rr,hasEncodingLoss:Ta,isTypedArray:hn,bytesPerElement:py,bytesFromStringArray:Rx,isString:qi,isBoolean:Ox,isNumber:ld,inferDtype:Aa,isFunction:Or,nearestDivisor:hd,computeStrides:je,toNestedArray:Ls,makeOnesTypedArray:my,makeZerosTypedArray:va,makeZerosNestedTypedArray:fy,assertNonNegativeIntegerDimensions:gy,locToIndex:Ws,indexToLoc:vo,isPromise:No});class Wk{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new Uk)}profileKernel(e,t,n){let s;const i=()=>{s=n()},o=this.backendTimer.time(i);for(let c=0;c<s.length;c++){const h=s[c];h.data().then(d=>{$k(d,h.dtype,e)})}const a={kernelName:e,outputs:s,inputs:t,timeMs:o.then(c=>c.kernelMs),extraInfo:o.then(c=>c.getExtraProfileInfo!=null?c.getExtraProfileInfo():"")};return a}logKernelProfile(e){const{kernelName:t,outputs:n,timeMs:s,inputs:i,extraInfo:o}=e;n.forEach(a=>{Promise.all([a.data(),s,o]).then(c=>{this.logger.logKernelProfile(t,a,c[0],c[1],i,c[2])})})}}function $k(e,t,n){if(t!=="float32")return!1;for(let s=0;s<e.length;s++){const i=e[s];if(isNaN(i)||!isFinite(i))return console.warn(`Found ${i} in the result of '${n}'`),!0}return!1}class Uk{logKernelProfile(e,t,n,s,i,o){const a=typeof s=="number"?pt(`${s}ms`,9):s.error,c=pt(e,25),h=t.rank,d=t.size,m=pt(t.shape.toString(),14);let f="";for(const b in i){const w=i[b];if(w!=null){const L=w.shape||t.shape,x=L.length;f+=`${b}: ${x}D ${x>0?L:""} `}}console.log(`%c${c} %c${a} %c${h}D ${m} %c${d} 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gradient: gradient function not found for ${o.kernelName}.`);const c=o.gradient(a);for(const h in o.inputs){if(!(h in c))throw new Error(`Cannot backprop through input ${h}. 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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Ii().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>rh(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Ii().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Ii().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Wa.print(this,e)}clone(){return this.throwIfDisposed(),Wa.clone(this)}toString(e=!1){const t=this.dataSync();return Pk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Wa.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ii().makeVariable(this,e,t,n)}}Object.defineProperty(ee,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class lh extends ee{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ae(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ii().disposeTensor(this),this.dataId=e.dataId,Ii().incRef(this,null)}dispose(){Ii().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(lh,Symbol.hasInstance,{value:e=>e instanceof ee&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var cb;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(cb||(cb={}));var lb;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(lb||(lb={}));var hb;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(hb||(hb={}));var ub;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ub||(ub={}));const Hk={float32:hb,int32:cb,bool:lb,complex64:ub};function $n(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Hk[e][t]}function jd(e){return $n(e,"int32")}function Yt(e,t){if(e.dtype===t.dtype)return[e,t];const n=$n(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function pT(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Kd(e,t){return t.some(n=>n.id===e.id)}function ji(e){const t=[],n=new Set;return mT(e,t,n),t}function mT(e,t,n){if(e==null)return;if(e instanceof ee){t.push(e);return}if(!qk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),mT(o,t,n))}}function qk(e){return Array.isArray(e)||typeof e=="object"}var jk=Object.freeze({__proto__:null,makeTypesMatch:Yt,assertTypesMatch:pT,isTensorInList:Kd,getTensorsInContainer:ji});class fT{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class hh{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new fT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Wk(this.backendInstance),!0}setupRegisteredKernels(){const e=Gd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Gd(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof y)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return hh.nextTensorId++}nextVariableId(){return hh.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return G.runKernelFunc(d=>d.cast(o,a),c,null,Na,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,f=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=ib(s,this.backendName);let L;if(w!=null)b=()=>{const v=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,N);const O=N.map(({dataId:E,shape:k,dtype:F})=>this.makeTensorFromDataId(E,k,F));if(d){let E=this.getTensorsForGradient(s,t,O);if(E==null){a==null&&(a=[]);const k=O.filter((F,U)=>a[U]);E=(o||[]).slice().concat(k)}h=this.saveTensorsForBackwardMode(E)}return O};else{const v=N=>{if(!d)return;h=N.map(O=>this.keep(this.clone(O)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,v));const O=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,O),O}}let x;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(x=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(x),c=x.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-f,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:x.timeMs,extraInfo:x.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=rb(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&qi(e[0])&&(i=e.map(c=>Hd(c)));const o=s.write(i,t,n),a=new ee(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=Rx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new ee(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new lh(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*py(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof lh||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=rb(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const f=n[m],b=va(f.size,f.dtype);return this.makeTensor(b,f.shape,f.dtype)}return d}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=ji(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(i instanceof ee,()=>"The result y returned by f() must be a tensor.");const o=Bk(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{const a={};a[i.id]=n==null?Kk(i.shape):n,Mk(a,o,h=>this.tidy(h),Xk);const c=t.map(h=>a[h.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(h=>{for(const d of h.saved)d.dispose()}),this.state.activeTape=null),{value:i,grads:c}})}customGrad(e){return A(Or(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(i=>i instanceof ee),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n;const s={};return t.forEach((i,o)=>{s[o]=i}),this.runKernelFunc((i,o)=>(n=e(...t,o),A(n.value instanceof ee,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(Or(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s,(i,o)=>{const a=n.gradFunc(i,o),c=Array.isArray(a)?a:[a];A(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),A(c.every(d=>d instanceof ee),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const h={};return c.forEach((d,m)=>{h[m]=()=>d}),h})}}readSync(e){const t=this.state.tensorInfo.get(e);return t.backend.readSync(e)}read(e){const t=this.state.tensorInfo.get(e);return t.backend.read(e)}async time(e){const t=Kn(),n=await this.backend.time(e);return n.wallMs=Kn()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new fT;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}hh.nextTensorId=0,hh.nextVariableId=0;function Kk(e){const t=my(P(e),"float32");return G.makeTensor(t,e,"float32")}function gT(){const e=Fx();if(e._tfengine==null){const t=new kx(e);e._tfengine=new hh(t)}return Ck(e._tfengine.ENV),Vk(()=>e._tfengine),e._tfengine}const G=gT();function Xk(e,t){const n={a:e,b:t};return G.runKernelFunc((s,i)=>{const o=s.add(e,t);return i([e,t]),o},n,null,Co)}function Jk(){return typeof navigator!="undefined"&&navigator!=null}function yT(){if(Jk()){const e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function db(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Zk=Object.freeze({__proto__:null,isMobile:yT,isBrowser:db});const Ki=oe();Ki.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")}),Ki.registerFlag("IS_BROWSER",()=>db()),Ki.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),Ki.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),Ki.registerFlag("PROD",()=>!1),Ki.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ki.getBool("DEBUG")),Ki.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),Ki.registerFlag("IS_TEST",()=>!1);function xi(e,t){let n=e;if(hn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||hn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&oe().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&bT(e,s,[]),s}function bT(e,t,n){if(n=n||[],!Array.isArray(e)&&!hn(e)){A(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}A(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),A(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)bT(e[i],s,n.concat(i))}function wT(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof ee)return wT(s,e.dtype,t,n),e;let i=Aa(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),wT(s,i,t,n),e==null||!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${h}'`)}const o=xi(e,i);!hn(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Er(e,i):te(e,[],a);return G.makeTensor(c,o,i)}function uh(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 LT="__op";function z(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+LT;const i=(...o)=>{G.startScope(n);try{const a=s(...o);return No(a)&&console.error("Cannot return a Promise inside of tidy."),G.endScope(a),a}catch(a){throw G.endScope(null),a}};return Object.defineProperty(i,"name",{value:n,configurable:!0}),i}function Qk(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");B(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return G.runKernelFunc(i,o,null,fd)}const Xi=z({complex_:Qk});function Dr(e,t,n,s){if(s==null&&(s=Aa(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){gy(t);const i=P(t),o=P(n);A(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],h=a===n.length-1?c!==P(t.slice(a)):!0;A(n[a]===t[a]||!h,()=>`Error creating a new Tensor. 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Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=pb[f.dtype],w=e.slice(i,i+d*b),L=f.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(f.dtype==="uint8"||f.dtype==="uint16"){m=new Float32Array(L.length);for(let x=0;x<L.length;x++){const v=L[x];m[x]=v*f.scale+f.min}}else if(f.dtype==="float16")s===void 0&&(s=oF()),m=s(L);else throw new Error(`Unsupported quantization type ${f.dtype} for weight type float32.`);else if(c==="int32"){if(f.dtype!=="uint8"&&f.dtype!=="uint16")throw new Error(`Unsupported quantization type ${f.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let x=0;x<L.length;x++){const v=L[x];m[x]=Math.round(v*f.scale+f.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const f=P(o.shape);m=[];for(let b=0;b<f;b++){const w=new Uint32Array(e.slice(i,i+Xd))[0];i+=Xd;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const 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fb=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function ST(e){return fb?Buffer.byteLength(e):new Blob([e]).size}function tF(e){if(fb)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 nF(e){if(fb){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 Zd(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 IT(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 dh(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, 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c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class en{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return en.instance==null&&(en.instance=new en),en.instance}static registerSaveRouter(e){en.getInstance().saveRouters.push(e)}static registerLoadRouter(e){en.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return en.getHandlers(e,"save")}static getLoadHandlers(e,t){return en.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?en.getInstance().loadRouters:en.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const aF=e=>en.registerSaveRouter(e),cF=e=>en.registerLoadRouter(e),gb=e=>en.getSaveHandlers(e),yb=(e,t)=>en.getLoadHandlers(e,t);const Qd="tensorflowjs",bb=1,Ro="models_store",kr="model_info_store";async function Lee(){const e=wb();return new Promise((t,n)=>{const s=e.deleteDatabase(Qd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function wb(){if(!oe().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 Lb(e){const t=e.result;t.createObjectStore(Ro,{keyPath:"modelPath"}),t.createObjectStore(kr,{keyPath:"modelPath"})}class Oo{constructor(e){if(this.indexedDB=wb(),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(Qd,bb);i.onupgradeneeded=()=>Lb(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Ro,"readonly"),c=a.objectStore(Ro),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=dh(t),c=o.transaction(kr,"readwrite");let h=c.objectStore(kr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(Ro,"readwrite");const f=m.objectStore(Ro),b=f.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(kr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=x=>(o.close(),s(b.error))}},d.onerror=f=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}Oo.URL_SCHEME="indexeddb://";const xT=e=>oe().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Oo.URL_SCHEME))?lF(e.slice(Oo.URL_SCHEME.length)):null;en.registerSaveRouter(xT),en.registerLoadRouter(xT);function lF(e){return new Oo(e)}function hF(e){return e.startsWith(Oo.URL_SCHEME)?e.slice(Oo.URL_SCHEME.length):e}class uF{constructor(){this.indexedDB=wb()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(Qd,bb);n.onupgradeneeded=()=>Lb(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(kr,"readonly"),o=i.objectStore(kr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.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=hF(e),new Promise((t,n)=>{const s=this.indexedDB.open(Qd,bb);s.onupgradeneeded=()=>Lb(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(kr,"readwrite"),a=o.objectStore(kr),c=a.get(e);let h;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const d=a.delete(e),m=()=>{h=i.transaction(Ro,"readwrite");const f=h.objectStore(Ro),b=f.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};d.onsuccess=m,d.onerror=f=>(m(),i.close(),n(c.error))}},c.onerror=d=>(i.close(),n(c.error)),o.oncomplete=()=>{h==null?i.close():h.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ti="/",Eo="tensorflowjs_models",TT="info",dF="model_topology",pF="weight_specs",mF="weight_data",fF="model_metadata";function See(){if(!oe().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=Eo+Ti;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=vT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function AT(e){return{info:[Eo,e,TT].join(Ti),topology:[Eo,e,dF].join(Ti),weightSpecs:[Eo,e,pF].join(Ti),weightData:[Eo,e,mF].join(Ti),modelMetadata:[Eo,e,fF].join(Ti)}}function vT(e){const t=e.split(Ti);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ti)}function gF(e){return e.startsWith(Do.URL_SCHEME)?e.slice(Do.URL_SCHEME.length):e}class Do{constructor(e){if(!oe().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or 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a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=nF(o),t}}Do.URL_SCHEME="localstorage://";const NT=e=>oe().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Do.URL_SCHEME))?yF(e.slice(Do.URL_SCHEME.length)):null;en.registerSaveRouter(NT),en.registerLoadRouter(NT);function yF(e){return new Do(e)}class bF{constructor(){A(oe().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),A(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Eo+Ti,n=Ti+TT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=vT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=gF(e);const t=AT(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 $a="://";class Ss{constructor(){this.managers={}}static getInstance(){return Ss.instance==null&&(Ss.instance=new Ss),Ss.instance}static registerManager(e,t){A(e!=null,()=>"scheme must not be undefined or null."),e.endsWith($a)&&(e=e.slice(0,e.indexOf($a))),A(e.length>0,()=>"scheme must not be an empty string.");const n=Ss.getInstance();A(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${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 '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function ep(e){if(e.indexOf($a)===-1)throw new Error(`The url string provided does not contain a scheme. 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s){const o=n+$a+i;t[o]=s[i]}}return t}async function LF(e){const t=ep(e),n=Ss.getManager(t.scheme);return n.removeModel(t.path)}async function SF(e,t){const n=!1;return CT(e,t,n)}async function IF(e,t){const n=!0;return CT(e,t,n)}class xF{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(oe().get("IS_BROWSER")){oe().setPlatform("browser",new xF);try{Ss.registerManager(Do.URL_SCHEME,new bF)}catch(e){}try{Ss.registerManager(Oo.URL_SCHEME,new uF)}catch(e){}}const TF={importFetch:()=>r2()};let Ua;function Iee(){Ua=null}function xee(e){Ua=e}function Tee(){return Ua}class AF{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return 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o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),B(o.shape,a.shape,"Error in logLoss: ");const h=Ce(1),d=Ce(s),m=qt(X(o,ls(be(a,d)))),f=X(Re(h,o),ls(be(Re(h,a),d))),b=Re(m,f);return tr(b,c,i)}const _M=z({logLoss_:FM});function WM(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")),B(i.shape,o.shape,"Error in meanSquaredError: ");const c=Dh(i,o);return tr(c,a,s)}const $M=z({meanSquaredError_:WM});function UM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");B(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Ci(s),o=X(s,n),a=Lp(Is(qt(dn(s))));return be(Re(i,o),a)}function BM(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")),B(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const d=Ce(s),m=Ce(1),f=Ce(.5);o=be(X(o,Re(m,d)),X(f,d))}const h=UM(o,a);return tr(h,c,i)}const MM=z({sigmoidCrossEntropy_:BM});function PM(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 ${n}`);const s=vi((i,o,a)=>{const c=!0,h=sw(o,[n],c),d=Re(Ae(o,"float32"),h);a([i,d]);const m=qt(X(d,i)),f=$e(m,[n]),b=(w,L)=>{const[x,v]=L,N=vn(w.shape,[n]);return[X(K(w,N),Re(Ae(x,"float32"),Is(v))),X(K(w,N),Re(Is(v),Ae(x,"float32")))]};return{value:f,gradFunc:b}});return s(e,t)}function zM(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")),B(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ce(s),m=Ce(1),f=Ce(o.shape[1]);o=be(X(o,Re(m,d)),We(d,f))}const h=PM(o,a);return tr(h,c,i)}const VM=z({softmaxCrossEntropy_:zM});const GM={fft:Oh,ifft:Qa,rfft:Eh,irfft:kp},YM={hammingWindow:zB,hannWindow:YA,frame:HA,stft:HB},Vr={flipLeftRight:XB,resizeNearestNeighbor:KA,resizeBilinear:jA,rotateWithOffset:ZB,cropAndResize:jB,nonMaxSuppression:eM,nonMaxSuppressionAsync:cM,nonMaxSuppressionWithScore:hM,nonMaxSuppressionWithScoreAsync:dM,nonMaxSuppressionPadded:mM,nonMaxSuppressionPaddedAsync:gM},JA={bandPart:LM,gramSchmidt:IM,qr:TM},HM={absoluteDifference:NM,computeWeightedLoss:tr,cosineDistance:RM,hingeLoss:EM,huberLoss:kM,logLoss:_M,meanSquaredError:$M,sigmoidCrossEntropy:MM,softmaxCrossEntropy:VM};class nr extends Fo{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 He(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 nw(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ce(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(nr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class Fh extends nr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:Q(()=>et(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:Q(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;Q(()=>{const d=be(X(c,this.rho),X(At(a),1-this.rho)),m=X(We(Nn(be(h,this.epsilon)),Nn(be(c,this.epsilon))),a),f=be(X(h,this.rho),X(At(m),1-this.rho));c.assign(d),h.assign(f);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}Fh.className="Adadelta",fe(Fh);class _h extends nr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:Q(()=>Ya(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;Q(()=>{const c=be(a,At(o));a.assign(c);const h=be(X(We(o,Nn(be(c,G.backend.epsilon()))),-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&He(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)}}_h.className="Adagrad",fe(_h);class Wh extends nr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Q(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(n).variable()}),s==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Q(()=>{const n=Re(1,this.accBeta1),s=Re(1,this.accBeta2);t.forEach((i,o)=>{const a=G.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Q(()=>et(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:Q(()=>et(a).variable(c))});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,f=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(At(h),1-this.beta2)),w=We(f,n),L=We(b,s);d.assign(f),m.assign(b);const x=be(X(We(w,be(Nn(L),this.epsilon)),-this.learningRate),a);a.assign(x)}),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&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&He(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),Q(()=>{this.accBeta1.assign(ti(this.beta1,this.iterations_+1)),this.accBeta2.assign(ti(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)}}Wh.className="Adam",fe(Wh);class $h extends nr{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Q(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),s==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Q(()=>{const n=Re(1,this.accBeta1),s=We(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=G.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:et(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:et(a).variable(c)});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,f=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=dn(h),L=Us(b,w);d.assign(f),m.assign(L);const x=be(X(We(s,n),We(f,be(L,this.epsilon))),a);a.assign(x)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&He(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}$h.className="Adamax",fe($h);class sc extends nr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=G.registeredVariables[n];Q(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=bn(Ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}sc.className="SGD",fe(sc);class Uh extends sc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ce(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:Q(()=>et(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;Q(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(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)}}Uh.className="Momentum",fe(Uh);class Bh extends nr{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=G.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=G.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:Q(()=>et(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:Q(()=>et(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:Q(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,h=this.accumulatedMoments[s].variable;Q(()=>{const d=be(X(c,this.decay),X(At(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,f=be(X(m,this.decay),X(a,1-this.decay)),b=We(X(a,this.learningRate),Nn(Re(d,be(At(f),this.epsilon)))),w=be(X(h,this.momentum),b);c.assign(d),m.assign(f),h.assign(w);const L=Re(i,w);i.assign(L)}else{const m=be(X(c,this.decay),X(At(a),1-this.decay)),f=be(X(h,this.momentum),We(X(a,this.learningRate),Nn(be(m,this.epsilon))));c.assign(m),h.assign(f);const b=Re(i,f);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&He(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&He(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&He(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)}}Bh.className="RMSProp",fe(Bh);class Go{static sgd(e){return new sc(e)}static momentum(e,t,n=!1){return new Uh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Bh(e,t,n,s,i)}static 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ss{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ss.className}}tm.className="GlorotNormal",fe(tm);class nm extends ss{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ss.className}}nm.className="HeNormal",fe(nm);class sm extends ss{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ss.className}}sm.className="HeUniform",fe(sm);class im extends ss{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ss.className}}im.className="LeCunNormal",fe(im);class rm extends ss{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ss.className}}rm.className="LeCunNormal",fe(rm);class iL extends Ps{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Pe("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return Q(()=>{if(e.length<2)throw new Pe("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const n=e[0]>e[1]?[e[1],e[0]]:e,s=Zp(n,0,1,"float32");let i=JA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}iL.className="Orthogonal",fe(iL);const Nv={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 Cv(e,t={}){return Vh(e,$s.getMap().classNameMap,t,"initializer")}function Xt(e){return Mw(e)}function Pt(e){if(typeof e=="string"){const t=e in Nv?Nv[e]:e;if(t==="GlorotNormal")return new tm;if(t==="GlorotUniform")return new em;if(t==="HeNormal")return new nm;if(t==="HeUniform")return new sm;if(t==="LeCunNormal")return new im;if(t==="LeCunUniform")return new rm;{const n={};return n.className=t,n.config={},Cv(n)}}else return e instanceof Ps?e:Cv(e)}function x3(){return new Zw}function T3(){return new Qp}function A3(e){return new Qw(e)}function v3(e){return new eL(e)}function N3(e){return new tL(e)}function C3(e){return new nL(e)}function R3(e){return new sL(e)}function O3(e){return new ss(e)}function E3(e){return new em(e)}function D3(e){return new tm(e)}function k3(e){return new nm(e)}function F3(e){return new sm(e)}function _3(e){return new im(e)}function W3(e){return new rm(e)}function $3(e){return new iL(e)}var U3=Object.freeze({__proto__:null,zeros:x3,ones:T3,constant:A3,randomUniform:v3,randomNormal:N3,truncatedNormal:C3,identity:R3,varianceScaling:O3,glorotUniform:E3,glorotNormal:D3,heNormal:k3,heUniform:F3,leCunNormal:_3,leCunUniform:W3,orthogonal:$3});let B3=0;function Rv(){return B3++}const om={};function am(e=""){return e in om||(om[e]=0),om[e]+=1,e+om[e].toString()}function rL(e){return Array.isArray(e)&&Array.isArray(e[0])}function cm(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Je(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new q(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function Nt(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new q(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function lm(e){let t=0;for(const n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,i)=>s*i);return t}const Ov="Variable";class oi{constructor(e,t="float32",n=Ov,s=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Rv(),n=n==null?Ov:n,this.originalName=Lv(n),this.name=Sv(this.originalName),this.trainable_=s,this.constraint=i,this.val=SA(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),M3(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function M3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Hee(e,t,n,s){return new oi(e,t,n,!0,s)}function qee(e,t,n){return new oi(dt(e),t,n)}function jee(e,t,n){return new oi(et(e),t,n)}function Kee(e,t,n){const s=ei(e);return new oi(s,t,n)}function Xee(e,t,n){const s=_n(e);return new oi(s,t,n)}function Jee(e,t,n){return new oi(bp(e),t,n)}function Zee(e,t,n,s,i,o="randomUniform"){return new oi(zo(e,t,n,s),s,o)}function Qee(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Pe(`randomNormal does not support dType ${s}.`);return new oi(kh(e,t,n,s,i),s,o)}function ete(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Pe(`randomNormalVariable does not support dType ${s}.`);return new oi(cw(e,t,n,s,i),s,o)}function tte(e,t){return e.write(t)}function nte(e,t){return e.write(be(e.read(),t))}function ste(e,t){return e.write(Re(e.read(),t))}function oL(e){return e.map(t=>t.read())}function aL(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function ite(e,t){const n=t.map(i=>i.read()),s=nw(e,n);return t.map(i=>s.grads[i.name])}class Ln{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 ai{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=Rv(),o!=null&&(this.originalName=Lv(o),this.name=Sv(this.originalName)),this.rank=t.length}}let P3=0;class hm{constructor(e,t){this.callArgs=t,this.id=P3++,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 z3=0;class lt extends Fo{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=z3++,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=rr(n)+"_"+am(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 ii(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return ns(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return ns(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ir(`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 ir(`Layer ${this.name} is not connected, no input to return.`);return ns(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new ir(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new ir(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return ns(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=kt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=kt(this.inputSpec);if(e.length!==t.length)throw new q(`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 q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new q(`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 q(`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 q(`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 h=Number(c),d=i.axes[c],m=h>=0?a[h]:a[a.length+h];if(d!=null&&[d,null].indexOf(m)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${h} of input shape to have value ${d} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],h=s.shape[a];if(c!=null&&h!=null&&c!==h)throw new q(`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=kt(e);let s=!0;for(const o of n)if(!(o instanceof ai)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ai){i=!1;break}if(s===i)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return jo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of kt(e))o.push(a.shape);this.build(ns(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=kt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=ns(c),this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=V3(e),a=this.computeOutputShape(o);let c;const h=G3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((d,m)=>new ai(h,d,this,kt(e),t,this.name,m)):c=new ai(h,a,this,kt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new Pe("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 ir(`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 ir(`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 ii(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return lm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return oL(e?this.trainableWeights:this.weights)}setWeights(e){Q(()=>{const t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${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=oL(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ae(o.shape,c.shape))throw new q(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}aL(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Pt("zeros"));const c=s.apply(t,n),h=new oi(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(h.read())),o==null&&(o=!0),o?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=kt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=kt(e);t=kt(t),n=kt(n),s=kt(s),i=cm(i),o=cm(o);const h=[],d=[],m=[];for(const f of c)h.push(f.sourceLayer),d.push(f.nodeIndex),m.push(f.tensorIndex);new hm({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let f=0;f<t.length;f++)t[f].sourceLayer=this,t[f].nodeIndex=this.inboundNodes.length-1,t[f].tensorIndex=f}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 V3(e){e=kt(e);const t=[];for(const n of e)t.push(n.shape);return ns(t)}function G3(e){return"float32"}function Ev(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],h=s.nodeIndices[o],d=Ev(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class cc extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:am("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 q("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 q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("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 ai(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new hm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`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}}}cc.className="InputLayer",fe(cc);function Dv(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new q("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;n==null&&(n="float32");const s=new cc({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}),i=s.inboundNodes[0].outputTensors;return i[0]}async function qr(e){if(e==null)return;const t=[],n=[],s=[];for(const i in e){const o=e[i];if(typeof o!="number"){const a=o;t.push(a.data()),n.push(i),s.push(a)}}if(t.length>0){const i=await Promise.all(t);for(let o=0;o<i.length;++o)e[n[o]]=i[o][0];He(s)}}function kv(e){if(e==null)return;for(const t in e){const n=e[t];typeof n!="number"&&n.dispose()}}var Fv;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(Fv||(Fv={}));const Y3=125;class lc{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}}class _v{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(const t of this.callbacks)t.setParams(e)}setModel(e){for(const t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainEnd(e)}}class H3 extends lc{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});const n=t.size==null?0:t.size;this.seen+=n;for(const s in t){const i=t[s];if(typeof i=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+i*n;else{let o;s in this.totals?o=this.totals[s]:this.totals[s]=0;const a=Q(()=>be(this.totals[s],X(i,n)));this.totals[s]=a,o!=null&&o.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(const n of this.params.metrics){if(this.totals[n]==null)continue;typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:Q(()=>{const s=X(We(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),bn(t[n])})}}}class Wv extends lc{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(const n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){const e=[],t=[],n=[];for(const i in this.history){const o=this.history[i];for(let a=0;a<o.length;++a)if(typeof o[a]!="number"){const c=o[a];e.push(c.data()),t.push(i),n.push(a)}}const s=await Promise.all(e);for(let i=0;i<s.length;++i){const o=this.history[t[i]][n[i]];o.dispose(),this.history[t[i]][n[i]]=s[i][0]}}}class $v extends lc{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Y3),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. 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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 Ei extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=am(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],Gr(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Gr(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 O=N.sourceLayer,E=N.nodeIndex,k=N.tensorIndex;this.outputLayers.push(O),this.outputLayersNodeIndices.push(E),this.outputLayersTensorIndices.push(k)}for(const N of this.inputs){const O=N.sourceLayer,E=N.nodeIndex,k=N.tensorIndex;As(E===0,"input layer has >1 nodes"),As(k===0,"input layer has >1 tensors"),this.inputLayers.push(O),this.inputLayersNodeIndices.push(E),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const O=this.inputLayers[N];if(!(O instanceof cc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${O.getClassName()}.`);this.inputNames.push(O.name),this.feedInputShapes.push(O.batchInputShape),this.feedInputNames.push(O.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,O,E,k,F,U)=>{(k==null||F==null||U==null)&&(k=N.sourceLayer,F=N.nodeIndex,U=N.tensorIndex);const $=k.inboundNodes[F];if(E.indexOf($)!==-1)throw new ii(`The tensor ${N.name} at layer "${k.name}" is part of a cycle.`);if(O.indexOf($)!==-1)return;this.containerNodes.add(Ei.nodeKey(k,F)),k.id in o||(o[k.id]=Object.keys(o).length),E.indexOf($)===-1&&E.push($);const Y=$.inboundLayers.length;for(let j=0;j<Y;j++){const Z=$.inputTensors[j],ie=$.inboundLayers[j],de=$.nodeIndices[j],he=$.tensorIndices[j];c(Z,O,E,ie,de,he)}for(O.push($);E.indexOf($)>=0;)E.splice(E.indexOf($),1);a.push($)},h=[],d=[];for(const N of this.outputs)c(N,h,d);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let O=t[N.id];const E=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];O=Math.max(O,E),s[N.outboundLayer.id]=O,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=O;for(let k=0;k<N.inboundLayers.length;k++){const F=N.inboundLayers[k],U=N.nodeIndices[k],$=F.inboundNodes[U],Y=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(O+1,Y),n[$.id]=$}}const f={};for(const N in t){const O=t[N];O in f||(f[O]=[]),f[O].push(n[N])}const b={};for(const N in s){const O=s[N];O in b||(b[O]=[]),b[O].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(Kp);this.layers=[];for(const N of w){const O=b[N];O.sort((E,k)=>{const F=o[E.id],U=o[k.id];return F<U?-1:F>U?1:0});for(const E of O)E instanceof Ei&&this.internalContainerRefs.push(E),this.layers.push(E)}this.layersByDepth=b,w=Object.keys(f).map(N=>parseInt(N,10)).sort(Kp);const L=this.inputs.slice(),x=[];for(const N of w)for(const O of f[N]){const E=O.outboundLayer;if(E!=null){for(const k of O.inputTensors)if(L.indexOf(k)===-1)throw new ii(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${E.name}". The following previous layers were accessed without issue: ${x}`);for(const k of O.outputTensors)L.push(k);x.push(E.name)}}this.nodesByDepth=f;const v=this.layers.map(N=>N.name);for(const N of v){const O=v.filter(E=>E===N).length;if(O!==1)throw new ii(`The name "${N}" is used ${O} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(v))}this.outboundNodes=[],this.inboundNodes=[],new hm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 q("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 q(`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("/"),h=c.slice(0,-2).concat([c[c.length-1]]);a=h.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new q(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new q(`${o.length} of ${s} weights are not set: ${o}`)}aL(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${wm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=fL(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return Q(()=>{e=kt(e);const n=new Xo;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Zh(this.outputs,n,t)})}computeMask(e,t){return Q(()=>{e=kt(e);let n;return t==null?n=Ho(null,e.length):n=kt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=cm(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(Kp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const x=h.inboundLayers[L],v=h.nodeIndices[L],N=h.tensorIndices[L],O=`${x.name}_${v}_${N}`,E=n[O];m.push(E)}const f=d.computeOutputShape(ns(m)),b=cm(f),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const x=`${d.name}_${w}_${L}`;n[x]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];As(c in n),i.push(n[c])}return ns(i)}runInternalGraph(e,t){t==null&&(t=Ho(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(Kp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,f=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of f)L.id in n&&w.push(n[L.id]);if(w.length===f.length){let L={},x,v,N,O;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[E,k]=w[0];L.mask==null&&(L.mask=k),N=kt(m.call(E,L)),O=kt(m.computeMask(E,k)),x=[E],v=[k]}else x=w.map(E=>E[0]),v=w.map(E=>E[1]),L.mask==null&&(L.mask=v),N=kt(m.call(x,L)),O=kt(m.computeMask(x,v));if(m.activityRegularizer)throw new Pe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let E=0;E<b.length;++E){const k=b[E],F=N[E],U=O[E];n[k.id]=[F,U]}}}}const i=[],o=[],a=[];for(const c of this.outputs){As(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[h,d]=n[c.id];a.push(h.shape),i.push(h),o.push(d)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Ei?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Ei.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 q(`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 q("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return Q(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Ei.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(),h=[];for(let m=0;m<o.inboundNodes.length;m++){const f=o.inboundNodes[m],b=Ei.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(f.callArgs)try{JSON.stringify(f.callArgs),w=f.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${f.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(f.inboundLayers.length>0){const L=[];for(let x=0;x<f.inboundLayers.length;x++){const v=f.inboundLayers[x],N=f.nodeIndices[x],O=f.tensorIndices[x],E=Ei.nodeKey(v,N);let k=t[E];k==null&&(k=0),L.push([v.name,k,O,w])}h.push(L)}}}const d={};d.name=o.name,d.className=a,d.config=c,d.inboundNodes=h,n.push(d)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],h=Ei.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,d,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],h=Ei.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,d,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(x,v){x.name in o?o[x.name].push(v):o[x.name]=[v]}function c(x,v){const N=[];let O;for(const E of v){const k=E[0],F=E[1],U=E[2];if(O=E[3]==null?{}:E[3],!(k in i)){a(x,v);return}const $=i[k];if($.inboundNodes.length<=F){a(x,v);return}const Y=$.inboundNodes[F];N.push(Y.outputTensors[U])}N.length>0&&x.apply(ns(N),O)}function h(x){const v=x.name,N=ci(x,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[v]=N;const O=x.inboundNodes;O.forEach(E=>{if(!(E instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${E}`);a(N,E)})}const d=t.name,m=t.layers;for(const x of m)h(x);for(;!Xz(o);)for(const x of m){const v=i[x.name];if(v.name in o){const N=o[v.name];delete o[v.name];for(const O of N)c(v,O)}}const f=[],b=[],w=t.inputLayers;for(const x of w){const v=x[0],N=x[1],O=x[2];As(v in i);const E=i[v],k=E.inboundNodes[N].outputTensors;f.push(k[O])}const L=t.outputLayers;for(const x of L){const v=x[0],N=x[1],O=x[2];As(v in i);const E=i[v],k=E.inboundNodes[N].outputTensors;b.push(k[O])}return new e({inputs:f,outputs:b,name:d})}get stateful(){if(this._stateful)throw new q("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(){Q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Xv(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: ${JSON.stringify(e)}`)}function Jv(e,t){return Xv(e,t,"classWeight")}function bte(e,t){return Xv(e,t,"sampleWeight")}async function Zv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=Q(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());He(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])}),hs(a,"float32")}else return null}function LV(e,t){return X(e,t)}const SV=32;function Qv(e,t){let n,s;const i=t;n=i.xs,s=i.ys,A(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=eN("input",e.inputNames,n),a=eN("output",e.outputNames,s),c=o[0].shape[0];A(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)})`),A(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 h=0;h<o.length;h++)A(o[h].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[h]} has ${o[h].shape[0]}; expected ${c} based on input ${e.inputNames[0]}.`);for(let h=0;h<a.length;h++)A(a[h].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[h]} has ${a[h].shape[0]}; expected ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function eN(e,t,n){if(n instanceof ee)return[n];if(Array.isArray(n))return A(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 q(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function IV(e){if(e.length===3)throw new Pe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function xV(e,t,n){const s=n.batchesPerEpoch!=null;if(A(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),A(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),A(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}`),A(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),A(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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o=pv(e.slice(0,n),s);i+=`_${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 L=0;L<this.inputs.length;++L)m.push({key:this.inputs[L],value:n[L]});const f=new Xo(m),b=Zh(this.outputs,f,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const x=this.lossFunctions[L];let v=x(s[L],b[L]);i[L]!=null&&(v=LV(v,i[L]));const N=jt(v);t.push(N),L===0?w=v:w=be(w,v)}for(let L=0;L<this.metricsTensors.length;++L){let x;if(this.outputs.length>1&&L<this.outputs.length)x=t[L];else{const v=this.metricsTensors[L][0],N=this.metricsTensors[L][1];x=jt(v(s[N],b[N]))}bn(x),o.push(x)}return w=jt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>Q(()=>{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 h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Xo(o),c=Zh(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=jt(d(i[h],c[h]));h===0?n=m:n=be(n,m),t.push(n)}for(let h=0;h<this.metricsTensors.length;++h){const d=this.metricsTensors[h][0],m=this.metricsTensors[h][1],f=jt(d(i[m],c[m]));t.push(f)}return t})}async fit(e,t,n={}){return CV(this,e,t,n)}async fitDataset(e,t){return xV(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 h of a){const d=await h.data();c.push(d[0])}return He(a),ns(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=ap().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-ap().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=rr(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=>rr(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]=rr(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[rr(ym(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>rr(ym(e)));{const e={};for(const t in this.metrics)e[t]=rr(ym(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=Jh(e.optimizer_config),n=ci(t);let s;if(typeof e.loss=="string")s=qo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>qo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=qo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>qo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=qo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=gb(e);if(h.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new q(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await mb(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:kV,generatedBy:`TensorFlow.js tfjs-layers v${wm}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const h="optimizer",{data:d,specs:m}=await mb(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=Zd([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Hv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Hv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}ar.className="Model",fe(ar);class oN extends ar{}oN.className="Functional",fe(oN);async function FV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Jh(n),i=ci(s,t);if(e.weightsManifest!=null){const o=await kT(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),He(o)}return i}async function _V(e,t){if(t==null&&(t={}),typeof e=="string"){const n=yb(e,t);if(n.length===0)n.push(tp(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return WV(e,void 0,t)}async function WV(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("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=ci(Jh(i),t,a),h=s.trainingConfig;if(h!=null&&c.loadTrainingConfig(h),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=$V(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(f=>f.tensor))}return c}function $V(e,t){const n=Jd(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 uc extends ar{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:am("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 q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof uc||e instanceof ar;let n;if(t){if(n=e,n.outputs.length!==1)throw new q("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 q("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 q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=Dv({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 q(`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 q("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=Ev(this.outputs[0])}this.inboundNodes=[],new hm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Ho(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(Nt(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 ar({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 ii("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 ii("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 ii("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 ii("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 q("Legacy serialization format not supported yet.");i=t}else A(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 uc))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=ci(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}uc.className="Sequential",fe(uc);function UV(e){return new ar(e)}function BV(e){return new uc(e)}function MV(e,t){return t==null&&(t={}),_V(e,t)}function aN(e){return Dv(e)}function PV(e,t){zs.registerCallbackConstructor(e,t)}class ds extends Fo{getConfig(){return{}}}class cN extends ds{apply(e,t=1){return f3(e,t)}}cN.className="elu",fe(cN);class lN extends ds{apply(e){return Cp(e)}}lN.className="selu",fe(lN);class hN extends ds{apply(e){return Ci(e)}}hN.className="relu",fe(hN);class uN extends ds{apply(e){return Q(()=>Bo(6,Ci(e)))}}uN.className="relu6",fe(uN);class dN extends ds{apply(e){return e}}dN.className="linear",fe(dN);class pN extends ds{apply(e){return Ai(e)}}pN.className="sigmoid",fe(pN);class mN extends ds{apply(e){return y3(e)}}mN.className="hardSigmoid",fe(mN);class fN extends ds{apply(e){return ja(e)}}fN.className="softplus",fe(fN);class gN extends ds{apply(e){return g3(e)}}gN.className="softsign",fe(gN);class yN extends ds{apply(e){return Va(e)}}yN.className="tanh",fe(yN);class SL extends ds{apply(e,t=-1){return Vo(e,t)}}SL.className="softmax",fe(SL);class bN extends ds{apply(e,t=-1){return Ip(e,t)}}bN.className="logSoftmax",fe(bN);class wN extends ds{apply(e,t=1){return Q(()=>Ai(e.mul(t)).mul(e))}}wN.className="swish",fe(wN);function Kr(e){return e.getClassName()}function IL(e,t={}){return Vh(e,$s.getMap().classNameMap,t,"activation")}function Xr(e){if(e==null){const t={};return t.className="linear",t.config={},IL(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},IL(t)}else return e instanceof ds?e:IL(e)}function xL(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class LN extends Fo{}class eu extends LN{constructor(e){super();xL(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return Q(()=>{let t=dt([1]);return this.hasL1&&(t=be(t,$e(X(this.l1,dn(e))))),this.hasL2&&(t=be(t,$e(X(this.l2,jh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}eu.className="L1L2",fe(eu);function zV(e){return xL(e),new eu({l1:e!=null?e.l1:null,l2:0})}function VV(e){return xL(e),new eu({l2:e!=null?e.l2:null,l1:0})}const SN={l1l2:"L1L2"};function Ct(e){return Mw(e)}function IN(e,t={}){return Vh(e,$s.getMap().classNameMap,t,"regularizer")}function zt(e){if(e==null)return null;if(typeof e=="string"){const t=e in SN?SN[e]:e,n={className:t,config:{}};return IN(n)}else return e instanceof LN?e:IN(e)}class TL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Je(e);let n=Ci(e);return this.maxValue!=null&&(n=Zn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}TL.className="ReLU",fe(TL);class AL 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=Je(e);return wp(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}AL.className="LeakyReLU",fe(AL);class vL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Pt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=zt(e.alphaRegularizer),this.alphaConstraint=gn(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 q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Nt(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Ln({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Je(e),Nh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Xt(this.alphaInitializer),alphaRegularizer:Ct(this.alphaRegularizer),alphaConstraint:fn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}vL.className="PReLU",fe(vL);class NL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Pe(`Non-default alpha value (${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=Je(e);return Ga(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}NL.className="ELU",fe(NL);class CL 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=Je(e);return n.mul(Hh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}CL.className="ThresholdedReLU",fe(CL);class RL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new SL().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Je(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}}RL.className="Softmax",fe(RL);function dc(e,t,n){if(typeof e=="number")return Ho(e,t);if(e.length!==t)throw new q(`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(!l3(i))throw new q(`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 li(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 Lm(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Hr([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function OL(e,t){return Q(()=>(Kt(t),t==="channelsFirst"?Ye(e,[0,2,3,1]):e))}function xN(e,t){return Q(()=>(Kt(t),t==="channelsFirst"?Ye(e,[0,2,3,4,1]):e))}function TN(e,t,n,s=1,i="valid",o,a=1){return Q(()=>{if(o==null&&(o=si()),Kt(o),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Ye(e,[0,2,1])),i==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=mp(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=Oi(c,n)),c})}function wte(e,t,n=1,s="valid",i,o=1){return Q(()=>(Kt(i),TN(e,t,null,n,s,i,o)))}function Lte(e,t,n=[1,1],s="valid",i,o){return Q(()=>(Kt(i),EL(e,t,null,n,s,i,o)))}function EL(e,t,n,s=[1,1],i="valid",o,a,c=null){return Q(()=>{if(o==null&&(o=si()),Kt(o),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=OL(e,o);if(i==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=xw({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Ye(h,[0,3,1,2])),h})}function Ste(e,t,n=[1,1,1],s="valid",i,o){return Q(()=>(Kt(i),AN(e,t,null,n,s,i,o)))}function AN(e,t,n,s=[1,1,1],i="valid",o,a){return Q(()=>{if(o==null&&(o=si()),Kt(o),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=xN(e,o);if(i==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=jb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=Oi(c,n)),o==="channelsFirst"&&(c=Ye(c,[0,4,1,2,3])),c})}class DL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DL.verifyArgs(t),this.rank=e,wn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=dc(t.kernelSize,e,"kernelSize"),this.strides=dc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,vs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Kt(this.dataFormat),this.activation=Xr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=gn(t.biasConstraint),this.biasRegularizer=zt(t.biasRegularizer),this.activityRegularizer=zt(t.activityRegularizer),this.dilationRate=dc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(As("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!zw(e.kernelSize,"number",1,3))throw new q(`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:Kr(this.activation),useBias:this.useBias,biasInitializer:Xt(this.biasInitializer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),biasConstraint:fn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class tu extends DL{constructor(e,t){super(e,t);this.kernel=null,tu.verifyArgs(t),this.filters=t.filters,wn(this.filters,"filters"),this.kernelInitializer=Pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=gn(t.kernelConstraint),this.kernelRegularizer=zt(t.kernelRegularizer)}build(e){e=Nt(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`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 Q(()=>{e=Je(e);let n;const s=this.bias==null?null:this.bias.read(),i=fv(this.activation.getClassName());if(i!=null&&this.rank===2)n=EL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=TN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=EL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=AN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Nt(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=li(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:Xt(this.kernelInitializer),kernelRegularizer:Ct(this.kernelRegularizer),kernelConstraint:fn(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 q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class nu extends tu{constructor(e){super(2,e);nu.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!zw(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}nu.className="Conv2D",fe(nu);class Sm extends tu{constructor(e){super(3,e);Sm.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Sm.className="Conv3D",fe(Sm);class kL extends nu{constructor(e){super(e);if(this.inputSpec=[new Ln({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Nt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ln({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Q(()=>{let n=Je(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],h=s[a],d=this.kernelSize[0],m=this.kernelSize[1],f=this.strides[0],b=this.strides[1],w=Lm(c,f,d,this.padding),L=Lm(h,b,m,this.padding),x=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,1]));let v=fp(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ye(v,[0,3,1,2])),this.bias!=null&&(v=Oi(v,this.bias.read(),this.dataFormat)),this.activation!=null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=Nt(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],h=this.strides[1];return t[n]=this.filters,t[s]=Lm(t[s],c,o,this.padding),t[i]=Lm(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}kL.className="Conv2DTranspose",fe(kL);class vN extends tu{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 q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=zt(t.depthwiseRegularizer),this.depthwiseConstraint=gn(t.depthwiseConstraint),this.pointwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=zt(t.pointwiseRegularizer),this.pointwiseConstraint=gn(t.pointwiseConstraint)}build(e){if(e=Nt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 q(`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 Ln({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Q(()=>{e=Je(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ye(e,[0,2,3,1])),n=dw(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Oi(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ye(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=Xt(this.depthwiseInitializer),e.pointwiseInitializer=Xt(this.pointwiseInitializer),e.depthwiseRegularizer=Ct(this.depthwiseRegularizer),e.pointwiseRegularizer=Ct(this.pointwiseRegularizer),e.depthwiseConstraint=fn(this.depthwiseConstraint),e.pointwiseConstraint=fn(this.pointwiseConstraint),e}}vN.className="SeparableConv";class FL extends vN{constructor(e){super(2,e)}}FL.className="SeparableConv2D",fe(FL);class Im extends tu{constructor(e){super(1,e);Im.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"&&!zw(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Im.className="Conv1D",fe(Im);class _L 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 Q(()=>{if(e=Je(e),this.dataFormat==="channelsLast"){const n=Jp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Jp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Jp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Jp(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}}_L.className="Cropping2D",fe(_L);class WL 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 Q(()=>{let n=Je(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ye(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Ye(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}}WL.className="UpSampling2D",fe(WL);function GV(e,t,n=[1,1],s="valid",i,o){return Q(()=>{i==null&&(i=si()),Kt(i);let a=OL(e,i);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=$o(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Ye(a,[0,3,1,2])),a})}class $L extends DL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=gn(e.depthwiseConstraint),this.depthwiseRegularizer=zt(e.depthwiseRegularizer)}build(e){if(e=Nt(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 Q(()=>{e=Je(e);let n=GV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Oi(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Nt(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=li(t,this.kernelSize[0],this.padding,this.strides[0]),o=li(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=Xt(this.depthwiseInitializer),e.depthwiseRegularizer=Ct(this.depthwiseRegularizer),e.depthwiseConstraint=fn(this.depthwiseRegularizer),e}}$L.className="DepthwiseConv2D",fe($L);function NN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("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 CN(e,t,n,s=!1,i,o,a=!1,c=!1){return Q(()=>{const h=t.shape.length;if(h<3)throw new q(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(ri(2,h));if(t=Ye(t,d),o!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=Qn(i,-1)),i=Ye(i,d)),s&&(t=Ts(t,0),i!=null&&(i=Ts(i,0)));const m=[];let f,b=n;const w=t.shape[0],L=ni(t);let x;i!=null&&(x=ni(i));for(let N=0;N<w;++N){const O=L[N],E=Q(()=>e(O,b));if(i==null)f=E[0],b=E[1];else{const k=Q(()=>{const F=x[N],U=_n(F).sub(F),$=E[0].mul(F).add(b[0].mul(U)),Y=b.map((j,Z)=>E[1][Z].mul(F).add(j.mul(U)));return{output:$,newStates:Y}});f=k.output,b=k.newStates}c&&m.push(f)}let v;if(c){const N=1;v=ts(m,N)}return[f,v,b]})}class Di extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Am({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("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 Ln({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 ri(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){rL(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 Q(()=>{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 Pe("Constants support is not implemented in RNN yet.");rL(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Ln({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new Pe("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(!ae(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new q(`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 Ln({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){Q(()=>{if(!this.stateful)throw new ir("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new q("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=>dt([n,s])):this.states_=[dt([n,this.cell.stateSize])];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>dt([n,s])):this.states_[0]=dt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):He(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(!ae(i.shape,a))throw new q(`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=>bn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=NN(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 h of n)this.stateSpec.push(new Ln({shape:h.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof ai;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const f=super.apply(h,t);return this.inputSpec=m,f}else return super.apply(e,t)}call(e,t){return Q(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Je(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 q(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,L)=>{const x=this.cell.call([w].concat(L),a);return[x[0],x.slice(1)]},h=CN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],f=h[2];this.stateful&&this.resetStates(f,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(f):b})}getInitialState(e){return Q(()=>{let t=dt(e.shape);return t=$e(t,[1,2]),t=qh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Xw(t,[1,n]):t):this.cell.stateSize>1?[Xw(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()===Di.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=ci(s,n);return new e(Object.assign(t,{cell:i}))}}Di.className="RNN",fe(Di);class pc extends lt{}class xm extends pc{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,wn(this.units,"units"),this.activation=Xr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=gn(e.kernelConstraint),this.recurrentConstraint=gn(e.recurrentConstraint),this.biasConstraint=gn(e.biasConstraint),this.dropout=ac([1,Hr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ac([1,Hr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Nt(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 Q(()=>{if(e=e,e.length!==2)throw new q(`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=Jr({ones:()=>_n(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Jr({ones:()=>_n(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Ri(X(e,o),this.kernel.read()):i=Ri(e,this.kernel.read()),this.bias!=null&&(i=Oi(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Ri(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:Kr(this.activation),useBias:this.useBias,kernelInitializer:Xt(this.kernelInitializer),recurrentInitializer:Xt(this.recurrentInitializer),biasInitializer:Xt(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),recurrentConstraint:fn(this.recurrentConstraint),biasConstraint:fn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}xm.className="SimpleRNNCell",fe(xm);class UL extends Di{constructor(e){e.cell=new xm(e),super(e)}call(e,t){return Q(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(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)}}UL.className="SimpleRNN",fe(UL);class Tm extends pc{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 q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,wn(this.units,"units"),this.activation=Xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=gn(e.kernelConstraint),this.recurrentConstraint=gn(e.recurrentConstraint),this.biasConstraint=gn(e.biasConstraint),this.dropout=ac([1,Hr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ac([1,Hr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Nt(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 Q(()=>{if(e=e,e.length!==2)throw new q(`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=Jr({ones:()=>_n(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Jr({ones:()=>_n(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Ri(e,this.kernel.read());this.useBias&&(d=Oi(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[f,b]=us(m,[2*this.units,this.units],m.rank-1),w=Ri(s,f),[L,x,v]=us(d,3,d.rank-1),[N,O]=us(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(x,O));const E=Ri(X(c,s),b);h=this.activation.apply(be(v,E));const k=be(X(a,s),X(be(1,qt(a)),h));return[k,k]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Kr(this.activation),recurrentActivation:Kr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Xt(this.kernelInitializer),recurrentInitializer:Xt(this.recurrentInitializer),biasInitializer:Xt(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),recurrentConstraint:fn(this.recurrentConstraint),biasConstraint:fn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}Tm.className="GRUCell",fe(Tm);class BL extends Di{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 Q(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(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)}}BL.className="GRU",fe(BL);class su extends pc{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,wn(this.units,"units"),this.activation=Xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=gn(e.kernelConstraint),this.recurrentConstraint=gn(e.recurrentConstraint),this.biasConstraint=gn(e.biasConstraint),this.dropout=ac([1,Hr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ac([1,Hr([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=Nt(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,h){const d=i.apply([o]),m=new Qp().apply([o]),f=i.apply([o*2]);return Tv(Tv(d,m),f)}},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 Q(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`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=Jr({ones:()=>_n(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Jr({ones:()=>_n(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,h,d,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let f=Ri(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),f=be(f,Ri(s,this.recurrentKernel.read())),this.useBias&&(f=Oi(f,this.bias.read()));const[b,w,L,x]=us(f,4,f.rank-1);c=this.recurrentActivation.apply(b),h=this.recurrentActivation.apply(w),d=be(X(h,i),X(c,this.activation.apply(L))),m=this.recurrentActivation.apply(x);const v=X(m,this.activation.apply(d));return[v,v,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Kr(this.activation),recurrentActivation:Kr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Xt(this.kernelInitializer),recurrentInitializer:Xt(this.recurrentInitializer),biasInitializer:Xt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),recurrentConstraint:fn(this.recurrentConstraint),biasConstraint:fn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}su.className="LSTMCell",fe(su);class ML extends Di{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 su(e),super(e)}call(e,t){return Q(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(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)}}ML.className="LSTM",fe(ML);class Am extends pc{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 Q(()=>{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){rL(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{jo(`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(ci(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 oL(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]])}aL(t)}}Am.className="StackedRNNCells",fe(Am);function Jr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>vv(t(),n),a=()=>Kh(o,t,s);if(!i||i<=1)return bn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>bn(h.clone()))}var YV=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 Ite extends pc{}class RN extends Di{constructor(e){if(e.unroll)throw new Pe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Pe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ln({ndim:5})]}call(e,t){return Q(()=>{if(this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("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 Q(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=dt(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){Q(()=>{if(!this.stateful)throw new ir("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 q("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(()=>dt(i)):this.states_=[dt(i)];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>dt(i)):this.states_[0]=dt(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):He(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],h=i;if(!ae(c.shape,h))throw new q(`State ${a} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>bn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",h=e[c?3:2],d=e[c?4:3],m=li(h,s[0],i,o[0],a[0]),f=li(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,f]:[m,f,n]];return b}}RN.className="ConvRNN2D";class vm extends su{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,wn(this.filters,"filters"),this.kernelSize=dc(n,2,"kernelSize"),this.kernelSize.forEach(c=>wn(c,"kernelSize")),this.strides=dc(s||1,2,"strides"),this.strides.forEach(c=>wn(c,"strides")),this.padding=i||"valid",vs(this.padding),this.dataFormat=o||"channelsLast",Kt(this.dataFormat),this.dilationRate=dc(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>wn(c,"dilationRate"))}build(e){var t;e=Nt(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`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 h=this.biasInitializer,d=this.filters;c=new(t=class extends Ps{apply(f,b){const w=h.apply([d]),L=ei([d]),x=h.apply([d*2]);return Kw([w,L,x])}},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 Q(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Jr({ones:()=>_n(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(we,Se,xe)=>!Se||!Se[xe]?we:X(Se[xe],we);let d=h(s,c,0),m=h(s,c,1),f=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Jr({ones:()=>_n(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),x=h(i,w,1),v=h(i,w,2),N=h(i,w,3);const O=3,[E,k,F,U]=us(this.kernel.read(),a,O),[$,Y,j,Z]=this.useBias?us(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,E,$,this.padding),m=this.inputConv(m,k,Y,this.padding),f=this.inputConv(f,F,j,this.padding),b=this.inputConv(b,U,Z,this.padding);const[ie,de,he,ue]=us(this.recurrentKernel.read(),a,O);L=this.recurrentConv(L,ie),x=this.recurrentConv(x,de),v=this.recurrentConv(v,he),N=this.recurrentConv(N,ue);const me=this.recurrentActivation.apply(be(d,L)),ce=this.recurrentActivation.apply(be(m,x)),ye=be(X(ce,o),X(me,this.activation.apply(be(f,v)))),pe=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[pe,pe,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=YV(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=Qi(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Oi(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return Qi(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}vm.className="ConvLSTM2DCell",fe(vm);class PL extends RN{constructor(e){const t=new vm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}PL.className="ConvLSTM2D",fe(PL);class Nm 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 Q(()=>{this.invokeCallHook(e,t);const n=Je(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Kh(()=>vv(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()}}Nm.className="Dropout",fe(Nm);class zL extends Nm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}zL.className="SpatialDropout1D",fe(zL);class VL 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,wn(this.units,"units"),this.activation=Xr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=gn(e.kernelConstraint),this.biasConstraint=gn(e.biasConstraint),this.kernelRegularizer=zt(e.kernelRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.activityRegularizer=zt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Nt(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=Nt(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Je(e),s=fv(this.activation.getClassName());let i;return s!=null?i=Ri(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Ri(n,this.kernel.read()),this.bias!=null&&(i=Oi(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Kr(this.activation),useBias:this.useBias,kernelInitializer:Xt(this.kernelInitializer),biasInitializer:Xt(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),biasConstraint:fn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}VL.className="Dense",fe(VL);class GL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Nt(e);for(const t of e.slice(1))if(t==null)throw new q(`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 Q(()=>{this.invokeCallHook(e,t);let n=Je(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 m3(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}GL.className="Flatten",fe(GL);class YL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Xr(e.activation)}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Je(e);return this.activation.apply(n)})}getConfig(){const e={activation:Kr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}YL.className="Activation",fe(YL);class HL 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 Q(()=>(e=Je(e),d3(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}HL.className="RepeatVector",fe(HL);class qL 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 h=s[c];if(this.isUnknown(h))if(o===null)o=c;else throw new q("Can only specifiy one unknown dimension.");else i*=h}const a=Yr(e);if(o!==null){if(i===0||a%i!==0)throw new q(n);s[o]=a/i}else if(a!==i)throw new q(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 Q(()=>{this.invokeCallHook(e,t);const n=Je(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}}qL.className="Reshape",fe(qL);class jL 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=ri(1,e.dims.length+1);if(!ae(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 Ln({ndim:this.dims.length+1})]}computeOutputShape(e){e=Nt(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ye(Je(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}jL.className="Permute",fe(jL);class KL 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=Je(e),s=-1;return mh(Mr(n,this.maskValue),s)}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Je(e),s=-1,i=!0,o=mh(Mr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}KL.className="Masking",fe(KL);class XL 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(kt(e.inputLength))}this.inputDim=e.inputDim,wn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,wn(this.outputDim,"outputDim"),this.embeddingsInitializer=Pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=zt(e.embeddingsRegularizer),this.activityRegularizer=zt(e.activityRegularizer),this.embeddingsConstraint=gn(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 Q(()=>this.maskZero?(e=Je(e),Mr(e,et(e))):null)}computeOutputShape(e){if(e=Nt(e),this.inputLength==null)return[...e,this.outputDim];const t=kt(this.inputLength);if(t.length!==e.length-1)throw new q(`"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 q(`"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 Q(()=>{this.invokeCallHook(e,t);let n=Je(e);n.dtype!=="int32"&&(n=Hh(n,"int32"));const s=Av(this.embeddings.read(),n.as1D());return s.reshape(Nt(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Xt(this.embeddingsInitializer),embeddingsRegularizer:Ct(this.embeddingsRegularizer),activityRegularizer:Ct(this.activityRegularizer),embeddingsConstraint:fn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}XL.className="Embedding",fe(XL);class Zo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Pe}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 q("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=[Nt(e)]),e=e,e.length<2)throw new q(`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=Gr(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Gr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return Q(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Hr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=qh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],f=d.slice(1).concat([m]);let b=c.reshape([m].concat(Yr(d.slice(1))));b=Ye(b,[1,0]),b=b.reshape(f),n.push(b),i=!0}else if(h>1){const d=ri(1,h).concat([0]);n.push(Ye(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Ye(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(ri(0,a-1));o=Ye(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Gr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return Q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Qn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Bs(n,t[s]);return n})}}class iu extends Zo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}iu.className="Add",fe(iu);function xte(e){if(Array.isArray(e)){const t=new iu({});return t.apply(e)}else return new iu(e)}class ru extends Zo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}ru.className="Multiply",fe(ru);function Tte(e){if(Array.isArray(e)){const t=new ru({});return t.apply(e)}else return new ru(e)}class ou extends Zo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}ou.className="Average",fe(ou);function Ate(e){if(Array.isArray(e)){const t=new ou({});return t.apply(e)}else return new ou(e)}class au extends Zo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Us(t,e[n]);return t})}}au.className="Maximum",fe(au);function vte(e){if(Array.isArray(e)){const t=new au({});return t.apply(e)}else return new au(e)}class cu extends Zo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Bo(t,e[n]);return t})}}cu.className="Minimum",fe(cu);function Nte(e){if(Array.isArray(e)){const t=new cu({});return t.apply(e)}else return new cu(e)}class lu extends Zo{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 q("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(ae(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return Q(()=>Kw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("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 q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return Q(()=>{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(_n(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Qn(t[o],-1)):s.push(t[o]);const i=Ht(s,this.axis);return lp(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}lu.className="Concatenate",fe(lu);function Cte(e){if(Array.isArray(e)){const t=new lu({});return t.apply(e)}else return new lu(e)}function hu(e,t){for(;e<0;)e+=t;return e}function HV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Pe("batchDot is not implemented for tensors of 4D or higher rank yet");if(A(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),A(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 Pe("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 Q(()=>{let a;if(s>i){a=s-i;const h=[];for(let d=0;d<a;++d)h.push(1);t=t.reshape(t.shape.concat(h))}else if(i>s){a=i-s;const h=[];for(let d=0;d<a;++d)h.push(1);e=e.reshape(e.shape.concat(h))}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 h=o[0]!==e.shape.length-1,d=o[1]===t.shape.length-1;c=e.matMul(t,h,d)}if(a>0){let h;s>i?h=s+i-3:h=s-1;const d=[];for(let m=h;m<h+a;++m)d.push(m);c=c.squeeze(d)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class JL extends Zo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){A(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 Pe("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 q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`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)=>hu(i,e[o].shape.length)):s=[hu(this.axes,t.shape.length),hu(this.axes,n.shape.length)],this.normalize&&(t=um(t,s[0]),n=um(n,s[1])),HV(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[hu(this.axes,e.length),hu(this.axes,t.length)],n}computeOutputShape(e){A(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 Pe("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}}JL.className="Dot",fe(JL);class ZL 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 Q(()=>{this.invokeCallHook(e,t);const n=Je(e),s=()=>Zp(n.shape,0,this.stddev).add(n),i=Kh(s,()=>n,t.training||!1);return i})}}ZL.className="GaussianNoise",fe(ZL);class QL 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 Q(()=>{this.invokeCallHook(e,t);const n=Je(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Zp(n.shape,1,i))};return Kh(s,()=>n,t.training||!1)}return n})}}QL.className="GaussianDropout",fe(QL);class eS extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Je(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 Q(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Je(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=er(zo(n),this.rate);h=Hh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,f=i.mul(h).add(h.add(-1).mul(c));return f.mul(d).add(m)};return Kh(s,()=>Je(e),t.training||!1)}return e})}}eS.className="AlphaDropout",fe(eS);function uu(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=eA(e,t,n,s,i,o);else if(e.rank===3)a=tA(e,t,n,s,i,o);else if(e.rank===4)a=nA(e,t,n,s,i,o);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function qV(e,t,n,s,i=.001){return Q(()=>{const o=Ap(e,s),a=o.mean,c=o.variance,h=uu(e,a,c,n,t,i);return[h,a,c]})}function jV(e,t,n,s,i=.001){return Q(()=>{const o=Ap(e,s),a=o.mean,c=o.variance,h=[];for(const L of ri(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),f=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=uu(e,d,m,b,f,i);return[w,a,c]})}function KV(e,t,n,s,i=.001){return ae(s.slice().sort(),ri(0,e.rank-1))?qV(e,t,n,s,i):jV(e,t,n,s,i)}class tS 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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=gn(e.betaConstraint),this.gammaConstraint=gn(e.gammaConstraint),this.betaRegularizer=zt(e.betaRegularizer),this.gammaRegularizer=zt(e.gammaRegularizer)}build(e){e=Nt(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ln({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 Q(()=>{const n=t.training==null?!1:t.training,s=Je(e),i=s.shape,o=i.length,a=ri(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=Ho(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ae(d,ri(0,o).slice(0,o-1)),f=()=>{if(m){const N=this.movingMean.read().reshape(h),O=this.movingVariance.read().reshape(h),E=this.center?this.beta.read().reshape(h):null,k=this.scale?this.gamma.read().reshape(h):null;return uu(s,N,O,E,k,this.epsilon)}else return uu(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 f();const[b,w,L]=KV(s,this.gamma.read(),this.beta.read(),a,this.epsilon),x=(N,O,E)=>{Q(()=>{const k=1-E,F=N.read(),U=F.sub(O).mul(k);N.write(F.sub(U))})},v=()=>{x(this.movingMean,w,this.momentum),x(this.movingVariance,L,this.momentum)};return v(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Xt(this.betaInitializer),gammaInitializer:Xt(this.gammaInitializer),movingMeanInitializer:Xt(this.movingMeanInitializer),movingVarianceInitializer:Xt(this.movingVarianceInitializer),betaRegularizer:Ct(this.betaRegularizer),gammaRegularizer:Ct(this.gammaRegularizer),betaConstraint:fn(this.betaConstraint),gammaConstraint:fn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}tS.className="BatchNormalization",fe(tS);class nS 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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.betaRegularizer=zt(e.betaRegularizer),this.gammaRegularizer=zt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Nt(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!==Gr(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=Je(e),s=n.shape,i=s.length;return Q(()=>{const o=!0;let{mean:a,variance:c}=Ap(n,this.axis,o);const h=Ho(1,i);for(const L of this.axis)h[L]=s[L];const d=L=>L!=null&&L.shape.length!==i&&this.axis!==[i-1]?L.reshape(h):L;let m=d(this.gamma.read()),f=d(this.beta.read());const b=[],w=[];for(let L=0;L<i;++L)this.axis.indexOf(L)!==-1?(b.push(s[L]),w.push(1)):(b.push(1),w.push(s[L]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),f=f.tile(w),uu(n,a,c,f,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Xt(this.betaInitializer),gammaInitializer:Xt(this.gammaInitializer),betaRegularizer:Ct(this.betaRegularizer),gammaRegularizer:Ct(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}nS.className="LayerNormalization",fe(nS);function Rte(e,t){return Q(()=>{if(e.rank!==3)throw new q(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new q(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return Ni(e,n)})}function XV(e,t,n){return Q(()=>{if(e.rank!==4)throw new q(`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 q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=si()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ni(e,s)})}class sS extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?si():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ln({ndim:4})]}computeOutputShape(e){e=Nt(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 Q(()=>XV(Je(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}sS.className="ZeroPadding2D",fe(sS);function Cm(e,t,n,s,i,o){return Q(()=>{Kt(i),bv(o),vs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=si()),o==null&&(o="max"),e=OL(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Ah(e,t,n,c):a=yh(e,t,n,c),i==="channelsFirst"&&(a=Ye(a,[0,3,1,2])),a})}function ON(e,t,n,s,i,o){return Q(()=>{Kt(i),bv(o),vs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=si()),o==null&&(o="max"),e=xN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=iw(e,t,n,c):a=Yb(e,t,n,c),i==="channelsFirst"&&(a=Ye(a,[0,4,1,2,3])),a})}class EN 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 q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(wn(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);wn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vs(this.padding),this.inputSpec=[new Ln({ndim:3})]}computeOutputShape(e){e=Nt(e);const t=li(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Q(()=>{this.invokeCallHook(e,t),e=qh(Je(e),2);const n=this.poolingFunction(Je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Pr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class iS extends EN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Kt(i),vs(s),Cm(e,t,n,s,i,"max")}}iS.className="MaxPooling1D",fe(iS);class rS extends EN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Kt(i),vs(s),Cm(e,t,n,s,i,"avg")}}rS.className="AveragePooling1D",fe(rS);class DN 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 q(`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];wn(this.poolSize,"poolSize"),wn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Kt(this.dataFormat),vs(this.padding),this.inputSpec=[new Ln({ndim:4})]}computeOutputShape(e){e=Nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=li(t,this.poolSize[0],this.padding,this.strides[0]),n=li(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 Q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Je(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 oS extends DN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Kt(i),vs(s),Cm(e,t,n,s,i,"max")}}oS.className="MaxPooling2D",fe(oS);class aS extends DN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Kt(i),vs(s),Cm(e,t,n,s,i,"avg")}}aS.className="AveragePooling2D",fe(aS);class kN 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 q(`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];wn(this.poolSize,"poolSize"),wn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Kt(this.dataFormat),vs(this.padding),this.inputSpec=[new Ln({ndim:5})]}computeOutputShape(e){e=Nt(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=li(t,this.poolSize[0],this.padding,this.strides[0]),n=li(n,this.poolSize[1],this.padding,this.strides[1]),s=li(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 Q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Je(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 cS extends kN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Kt(i),vs(s),ON(e,t,n,s,i,"max")}}cS.className="MaxPooling3D",fe(cS);class lS extends kN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Kt(i),vs(s),ON(e,t,n,s,i,"avg")}}lS.className="AveragePooling3D",fe(lS);class FN extends lt{constructor(e){super(e);this.inputSpec=[new Ln({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}}class hS extends FN{constructor(e){super(e||{})}call(e,t){return Q(()=>{const n=Je(e);return jt(n,1)})}}hS.className="GlobalAveragePooling1D",fe(hS);class uS extends FN{constructor(e){super(e||{})}call(e,t){return Q(()=>{const n=Je(e);return es(n,1)})}}uS.className="GlobalMaxPooling1D",fe(uS);class _N extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Kt(this.dataFormat),this.inputSpec=[new Ln({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class dS extends _N{call(e,t){return Q(()=>{const n=Je(e);return this.dataFormat==="channelsLast"?jt(n,[1,2]):jt(n,[2,3])})}}dS.className="GlobalAveragePooling2D",fe(dS);class pS extends _N{call(e,t){return Q(()=>{const n=Je(e);return this.dataFormat==="channelsLast"?es(n,[1,2]):es(n,[2,3])})}}pS.className="GlobalMaxPooling2D",fe(pS);class WN 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=ci(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class mS extends WN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Nt(e),e.length<3)throw new q(`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=Nt(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 Q(()=>{e=Je(e);const n=(o,a)=>{const c=Je(this.layer.call(o,t));return[c,[]]},s=CN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}mS.className="TimeDistributed",fe(mS);function JV(e){rc(o3,"BidirectionalMergeMode",e)}const ZV="concat";class fS extends WN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ci(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ci(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?ZV:e.mergeMode,JV(this.mergeMode),e.weights)throw new Pe("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()):ns(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=NN(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 h=n.length;if(h%2>0)throw new q("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 d=n.map(m=>new Ln({shape:m.shape}));this.forwardLayer.stateSpec=d.slice(0,h/2),this.backwardLayer.stateSpec=d.slice(h/2),a.push(...d)}if(s!=null)throw new Pe("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ai;for(const h of o)if(h instanceof ai!==c)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const f=super.apply(h,t);return this.inputSpec=m,f}else return super.apply(e,t)}call(e,t){return Q(()=>{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),h=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:h}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=Ts(i,1));let a;return this.mergeMode==="concat"?a=Kw([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){jo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),jo(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=ci(t.layer);if(delete t.layer,t.numConstants!=null)throw new Pe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}fS.className="Bidirectional",fe(fS);function QV(e){return new cc(e)}function eG(e){return new NL(e)}function tG(e){return new TL(e)}function nG(e){return new AL(e)}function sG(e){return new vL(e)}function iG(e){return new RL(e)}function rG(e){return new CL(e)}function oG(e){return new Im(e)}function aG(e){return new nu(e)}function cG(e){return new kL(e)}function 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ZY=Object.freeze({__proto__:null,json:JY});const QY=[{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 eH=Object.freeze({__proto__:null,json:QY});const tH=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var nH=Object.freeze({__proto__:null,json:tH});const sH=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var iH=Object.freeze({__proto__:null,json:sH});const rH=[{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 oH=Object.freeze({__proto__:null,json:rH});const aH=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];var cH=Object.freeze({__proto__:null,json:aH});const lH=[{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 hH=Object.freeze({__proto__:null,json:lH});const uH=[{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 dH=Object.freeze({__proto__:null,json:uH});const pH=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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 mH=Object.freeze({__proto__:null,json:pH});class HN{static get Instance(){return this._instance||(this._instance=new this)}constructor(){const e=[WY,UY,MY,zY,GY,HY,jY,nH,eH,XY,iH,oH,cH,hH,dH,mH,ZY],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,s)=>(n[s.tfOpName]=s,n),{})}transformGraph(e,t={}){const n=e.node,s=[],i=[],o=[],a=n.reduce((L,x)=>(L[x.name]=this.mapNode(x),x.op.startsWith("Placeholder")?s.push(L[x.name]):x.op==="Const"?i.push(L[x.name]):(x.input==null||x.input.length===0)&&o.push(L[x.name]),L),{});let c=[];const h=[];let d={},m={};t!=null&&(d=this.mapSignatureEntries(t.inputs),m=this.mapSignatureEntries(t.outputs));const f=Object.keys(a);f.forEach(L=>{const x=a[L];x.inputNames.forEach(v=>{const[N]=cr(v);x.inputs.push(a[N]),a[N].children.push(x)})}),Object.keys(m).length===0?f.forEach(L=>{const x=a[L];x.children.length===0&&h.push(x)}):Object.keys(m).forEach(L=>{const[x]=cr(L),v=a[x];v!=null&&(v.signatureKey=m[L],h.push(v))}),Object.keys(d).length>0?Object.keys(d).forEach(L=>{const[x]=cr(L),v=a[x];v&&(v.signatureKey=d[L],c.push(v))}):c=s;let b={};e.library!=null&&e.library.function!=null&&(b=e.library.function.reduce((L,x)=>(L[x.signature.name]=this.mapFunction(x),L),{}));const w={nodes:a,inputs:c,outputs:h,weights:i,placeholders:s,signature:t,functions:b};return o.length>0&&(w.initNodes=o),w}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){const t=YN(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});const n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,i)=>(s[i.name]={type:i.type,inputIndexStart:i.start,inputIndexEnd:i.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,i)=>{const o=i.type;let a;switch(i.type){case"string":a=LS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=LS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"string[]":a=CS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=CS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"number":a=IS(e.attr,i.tfName,i.defaultValue||0),a===void 0&&!!i.tfDeprecatedName&&(a=IS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"number[]":a=NS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=NS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"bool":a=SS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=SS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"bool[]":a=OS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=OS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"shape":a=vS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=vS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"shape[]":a=RS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=RS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"dtype":a=TS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=TS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"dtype[]":a=AS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=AS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"func":a=jN(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=jN(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${i.type} for op: ${e.op}`)}return s[i.name]={value:a,type:o},s},{})),n}mapFunction(e){const t=e.nodeDef,n=[],s=[];let i={};t!=null&&(i=t.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op==="Const"&&s.push(m[f.name]),m),{}));const o=[],a=[];e.signature.inputArg.forEach(m=>{const[f]=cr(m.name),b={name:f,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:xS(m.type),type:"dtype"}},children:[]};b.signatureKey=m.name,o.push(b),i[f]=b});const c=Object.keys(i);c.forEach(m=>{const f=i[m];f.inputNames.forEach(b=>{const[w]=cr(b);f.inputs.push(i[w]),i[w].children.push(f)})});const h=e.ret;e.signature.outputArg.forEach(m=>{const[f,b]=cr(h[m.name]),w=i[f];w!=null&&(w.defaultOutput=b,a.push(w))});const d=this.mapArgsToSignature(e);return{nodes:i,inputs:o,outputs:a,weights:s,placeholders:n,signature:d}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}}function fH(e){const t=oe().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function qN(e,t){const n=Array.isArray(e)?String.fromCharCode.apply(null,e):fH(e);return t?n:n.toLowerCase()}function LS(e,t,n,s=!1){const i=e[t];return i!=null?qN(i.s,s):n}function SS(e,t,n){const s=e[t];return s?s.b:n}function IS(e,t,n){const s=e[t]||{},i=s.i!=null?s.i:s.f!=null?s.f:n;return typeof i=="number"?i:parseInt(i,10)}function xS(e){typeof e=="string"&&(e=hi[e]);switch(e){case hi.DT_FLOAT:return"float32";case hi.DT_INT32:case hi.DT_INT64:case hi.DT_INT8:case hi.DT_UINT8:return"int32";case hi.DT_BOOL:return"bool";case hi.DT_DOUBLE:return"float32";case hi.DT_STRING:return"string";default:return null}}function jN(e,t,n){const s=e[t];return s&&s.func?s.func.name:n}function TS(e,t,n){const s=e[t];return s&&s.type?xS(s.type):n}function AS(e,t,n){const s=e[t];return s&&s.list&&s.list.type?s.list.type.map(i=>xS(i)):n}function KN(e){return e.unknownRank?void 0:e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function vS(e,t,n){const s=e[t];return s&&s.shape?KN(s.shape):n}function NS(e,t,n){const s=e[t];return s?((s.list.f&&s.list.f.length?s.list.f:s.list.i)||[]).map(i=>typeof i=="number"?i:parseInt(i,10)):n}function CS(e,t,n,s=!1){const i=e[t];return i&&i.list&&i.list.s?i.list.s.map(o=>qN(o,s)):n}function RS(e,t,n){const s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(i=>KN(i)):n}function OS(e,t,n){const s=e[t];return s&&s.list&&s.list.b?s.list.b:n}class gH{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(s=>this.getInput(s)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((s,i)=>(s[i]=this.getAttr(i),s),{}))}getInput(e){return is(e,this.tensorMap,this.context)}getAttr(e,t){const n=this.node.rawAttrs[e];if(n.tensor!=null)return is(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return IS(this.node.rawAttrs,e,t);if(n.s!=null)return LS(this.node.rawAttrs,e,t);if(n.b!=null)return SS(this.node.rawAttrs,e,t);if(n.shape!=null)return vS(this.node.rawAttrs,e,t);if(n.type!=null)return TS(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return NS(this.node.rawAttrs,e,t);if(n.list.s!=null)return CS(this.node.rawAttrs,e,t);if(n.list.shape!=null)return RS(this.node.rawAttrs,e,t);if(n.list.b!=null)return OS(this.node.rawAttrs,e,t);if(n.list.type!=null)return AS(this.node.rawAttrs,e,t)}return t}}const yH=(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[ZT(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[We(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[Jb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[cp(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Re(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[Bo(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[ti(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[Dh(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ete="arithmetic";const bH=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[dn(R("x",e,t,n))];case"Acos":return[Fb(R("x",e,t,n))];case"Acosh":return[_b(R("x",e,t,n))];case"Asin":return[Ub(R("x",e,t,n))];case"Asinh":return[Bb(R("x",e,t,n))];case"Atan":return[Mb(R("x",e,t,n))];case"Atan2":return[Pb(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[zb(R("x",e,t,n))];case"Ceil":return[Hb(R("x",e,t,n))];case"Complex":return[Xi(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[Lh(R("x",e,t,n))];case"Cosh":return[gp(R("x",e,t,n))];case"Elu":return[Ga(R("x",e,t,n))];case"Erf":return[Zb(R("x",e,t,n))];case"Exp":return[Is(R("x",e,t,n))];case"Expm1":return[Qb(R("x",e,t,n))];case"Floor":return[Ha(R("x",e,t,n))];case"Log":return[ls(R("x",e,t,n))];case"Log1p":return[Lp(R("x",e,t,n))];case"Imag":return[Ih(R("x",e,t,n))];case"Neg":return[qt(R("x",e,t,n))];case"Reciprocal":return[lw(R("x",e,t,n))];case"Real":return[Xa(R("x",e,t,n))];case"Relu":return[Ci(R("x",e,t,n))];case"Round":return[uw(R("x",e,t,n))];case"Selu":return[Cp(R("x",e,t,n))];case"Sigmoid":return[Ai(R("x",e,t,n))];case"Sin":return[Rp(R("x",e,t,n))];case"Sign":return[pw(R("x",e,t,n))];case"Sinh":return[Op(R("x",e,t,n))];case"Softplus":return[ja(R("x",e,t,n))];case"Sqrt":return[Nn(R("x",e,t,n))];case"Square":return[At(R("x",e,t,n))];case"Tanh":return[Va(R("x",e,t,n))];case"Tan":return[gw(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[Zn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[Np(is(e.inputNames[0],t,n))];case"Prod":return[vp(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[wp(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[Nh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Dte="basic_math";function Vs(e,t,n=""){A(wH(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function wH(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 LH{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=Ce(0),bn(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=[];Q(()=>{t=K(t,[1,n,i]);for(let c=0;c<e.length;++c){const h=c===0?0:s[c-1],d=[0,h,0],m=[1,e[c],i];o[c]=K(tt(t,d,m),this.elementShape)}return o});const a=[];for(let c=0;c<e.length;c++)a[c]=c;this.writeMany(a,o)}}class du{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}`);Vs(t,i.shape,"TensorList shape mismatch: "),bn(i)}),this.idTensor=Ce(0),this.maxNumElements=s,bn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new du([...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 Vs(e,this.elementShape,"TensorList shape mismatch: "),Q(()=>{const s=this.tensors.map(i=>K(i,e));return ts(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 Vs(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(Vs(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");bn(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 Vs(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.`);Vs(this.elementShape,t.shape,"TensorList shape mismatch: "),bn(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 Vs(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?sn([],[0].concat(this.elementShape)):Q(()=>{const s=e.map(i=>K(this.tensors[i],n));return ts(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Vs(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?sn([],[0].concat(this.elementShape)):Q(()=>{const n=this.tensors.map(s=>K(s,t));return Ht(n,0)})}}function SH(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);Vs(i,t,"TensorList shape mismatch: ");const o=ni(e);return new du(o,t,s)}function IH(e,t,n){return new du([],e,t,n)}function xH(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 du([],n,e.dtype,s),a=ni(e,0);return t.forEach((c,h)=>{o.setItem(c,a[h])}),o}function TH(e,t,n){let s=0;const i=t.map(h=>(s+=h,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=Q(()=>{const h=[];e=K(e,[1,s,o]);for(let d=0;d<t.length;++d){const m=d===0?0:i[d-1],f=[0,m,0],b=[1,t[d],o];h[d]=K(tt(e,f,b),n)}return e.dispose(),h}),c=new du([],n,e.dtype,t.length);for(let h=0;h<a.length;h++)c.setItem(h,a[h]);return c}const AH=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 h=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let d=o;for(;h[0];){const m=d;d=await n.functionMap[s].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);const f=d.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&f.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);h=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&f.indexOf(w.id)===-1&&w.dispose()})}return d}case"LoopCond":{const s=R("pred",e,t,n);return[lr(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=lr(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>is(i,t,n)!==void 0);if(s){const i=is(s,t,n);return[lr(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[lr(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[lr(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[lr(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),h=R("identicalElementShapes",e,t,n),d=R("name",e,t,n),m=new LH(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ce(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 s=R("tensorArrayId",e,t,n),i=R("tensor",e,t,n),o=R("lengths",e,t,n),a=n.getTensorArray(s.id);return a.split(o,i),[a.idTensor]}case"TensorArraySizeV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return[Ce(i.size(),"int32")]}case"TensorArrayCloseV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return i.clearAndClose(),[i.idTensor]}case"TensorListSetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorList(s.id);return a.setItem(i,o),[a.idTensor]}case"TensorListGetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.getItem(i,o,a)]}case"TensorListScatterV2":case"TensorListScatter":{const s=R("indices",e,t,n),i=R("tensor",e,t,n),o=R("elementShape",e,t,n),a=R("numElements",e,t,n),c=xH(i,s,o,a);return n.addTensorList(c),[c.idTensor]}case"TensorListReserve":{const s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=IH(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=SH(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 s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=n.getTensorList(s.id);return[a.popBack(i,o)]}case"TensorListSplit":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("lengths",e,t,n),a=TH(s,o,i);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},kte="control";function XN(e,t,n){const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=s==="fusedbatchnorm",h=R("numArgs",e,t,n);if(o){if(a&&h!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&h!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(c)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const d=R("strides",e,t,n),m=Em(e,t,n),f=R("dataFormat",e,t,n).toUpperCase(),b=R("dilations",e,t,n),[w,L]=R("args",e,t,n);return{stride:d,pad:m,dataFormat:f,dilations:b,biasArg:w,preluArg:L,activationFunc:i}}const vH=(e,t,n)=>{switch(e.op){case"Conv1D":{const s=R("stride",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilation",e,t,n);return[mp(R("x",e,t,n),R("filter",e,t,n),s,i,o,a)]}case"Conv2D":{const s=R("strides",e,t,n),i=Em(e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[Qi(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,o,[a[1],a[2]])]}case"_FusedConv2D":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=XN(e,t,n);return[xw({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"FusedDepthwiseConv2dNative":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=XN(e,t,n);return[GA({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const s=R("outputShape",e,t,n),i=R("strides",e,t,n),o=Em(e,t,n);return[fp(R("x",e,t,n),R("filter",e,t,n),s,[i[1],i[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const s=R("strides",e,t,n),i=Em(e,t,n),o=R("dilations",e,t,n),a=R("dataFormat",e,t,n).toUpperCase();return[$o(R("input",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,a,[o[1],o[2]])]}case"Conv3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[jb(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2],s[3]],i,o,[a[1],a[2],a[3]])]}case"AvgPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[yh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Ah(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPoolWithArgmax":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n),a=R("includeBatchInIndex",e,t,n),{result:c,indexes:h}=gA(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i,a);return[c,h]}case"AvgPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Yb(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"MaxPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[iw(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"Dilation2D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dilations",e,t,n),a=s[1],c=s[2],h=o[1],d=o[2];return[Xb(R("x",e,t,n),R("filter",e,t,n),[a,c],i,[h,d],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Fte="convolution";const NH=(e,t,n)=>{switch(e.op){case"Fill":{const s=R("shape",e,t,n),i=R("dtype",e,t,n),o=R("value",e,t,n);return[Ya(s,o,i)]}case"LinSpace":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("num",e,t,n);return[pA(s,i,o)]}case"Multinomial":{const s=R("logits",e,t,n),i=R("numSamples",e,t,n),o=R("seed",e,t,n);return[yA(s,i,o)]}case"OneHot":{const s=R("indices",e,t,n),i=R("depth",e,t,n),o=R("onValue",e,t,n),a=R("offValue",e,t,n);return[ko(s,i,o,a)]}case"Ones":return[ei(R("shape",e,t,n),R("dtype",e,t,n))];case"OnesLike":return[_n(R("x",e,t,n))];case"RandomUniform":return[zo(R("shape",e,t,n),R("minval",e,t,n),R("maxval",e,t,n),R("dtype",e,t,n))];case"Range":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("step",e,t,n);return[Ch(s,i,o,R("dtype",e,t,n))]}case"TruncatedNormal":{const s=R("shape",e,t,n),i=R("mean",e,t,n),o=R("stdDev",e,t,n),a=R("seed",e,t,n);return[kh(s,i,o,R("dtype",e,t,n),a)]}case"Zeros":return[dt(R("shape",e,t,n),R("dtype",e,t,n))];case"ZerosLike":return[et(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_te="creation";function ES(e,t,n){const s=R("boxes",e,t,n),i=R("scores",e,t,n),o=R("maxOutputSize",e,t,n),a=R("iouThreshold",e,t,n),c=R("scoreThreshold",e,t,n),h=R("softNmsSigma",e,t,n);return{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}}const CH=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=ES(e,t,n),d=await Vr.nonMaxSuppressionWithScoreAsync(s,i,o,a,c,h);return[d.selectedIndices,d.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=ES(e,t,n),h=R("padToMaxOutputSize",e,t,n),d=await Vr.nonMaxSuppressionPaddedAsync(s,i,o,a,c,h);return[d.selectedIndices,d.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=ES(e,t,n);return[await Vr.nonMaxSuppressionAsync(s,i,o,a,c)]}case"Where":{const s=Ae(R("condition",e,t,n),"bool"),i=[await ww(s)];return s.dispose(),i}case"ListDiff":return wA(R("x",e,t,n),R("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Wte="dynamic";const RH=(e,t,n)=>{switch(e.op){case"TopKV2":{const s=R("x",e,t,n),i=R("k",e,t,n),o=R("sorted",e,t,n),a=yw(s,i,o);return[a.values,a.indices]}case"Unique":{const s=R("x",e,t,n),i=Fp(s);return[i.values,i.indices]}case"UniqueV2":{const s=R("x",e,t,n),i=R("axis",e,t,n),o=Fp(s,i);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$te="evaluation";const OH=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=R("default",e,t,n);return[is(e.name,t,n)||s];case"Placeholder":return[is(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{const d=R("x",e,t,n);return[lr(d)]}case"IdentityN":return R("x",e,t,n).map(d=>lr(d));case"Snapshot":const i=R("x",e,t,n);return[lr(i)];case"Shape":return[hs(R("x",e,t,n).shape,"int32")];case"ShapeN":return R("x",e,t,n).map(d=>hs(d.shape));case"Size":return[Ce(R("x",e,t,n).size,"int32")];case"Rank":return[Ce(R("x",e,t,n).rank,"int32")];case"NoOp":return[Ce(1)];case"Print":const o=R("x",e,t,n),a=R("data",e,t,n),c=R("message",e,t,n),h=R("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(c);for(let d=0;d<a.length;d++)console.log(Array.prototype.slice.call(a[d].dataSync()).slice(0,h));return[o];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ute="graph";class EH{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ce(0),this.tensorMap=new Map,bn(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),Q(()=>{const s=ni(t),i=n.length,o=s.length;A(i===o,()=>`The number of elements doesn't match, keys has ${i} elements, the values has ${o} elements.`);for(let a=0;a<i;a++){const c=n[a],h=s[a];bn(h),this.tensorMap.set(c,h)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return Q(()=>{const s=[];for(let i=0;i<n.length;i++){const o=n[i],a=this.findWithDefault(o,t);s.push(a)}return ts(s)})}findWithDefault(e,t){const n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}}const DH=async(e,t,n,s)=>{switch(e.op){case"HashTable":case"HashTableV2":{const i=R("keyDType",e,t,n),o=R("valueDType",e,t,n),a=new EH(i,o);return s.addHashTable(e.name,a),[a.handle]}case"LookupTableImport":case"LookupTableImportV2":{const i=R("tableHandle",e,t,n,s),o=R("keys",e,t,n),a=R("values",e,t,n),c=s.getHashTableById(i.id);return[await c.import(o,a)]}case"LookupTableFind":case"LookupTableFindV2":{const i=R("tableHandle",e,t,n,s),o=R("keys",e,t,n),a=R("defaultValue",e,t,n),c=s.getHashTableById(i.id);return[await c.find(o,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Bte="hash_table";const kH=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[Vr.resizeBilinear(s,[i[0],i[1]],o)]}case"ResizeNearestNeighbor":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[Vr.resizeNearestNeighbor(s,[i[0],i[1]],o)]}case"CropAndResize":{const s=R("image",e,t,n),i=R("boxes",e,t,n),o=R("boxInd",e,t,n),a=R("cropSize",e,t,n),c=R("method",e,t,n),h=R("extrapolationValue",e,t,n);return[Vr.cropAndResize(s,i,o,a,c,h)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mte="image";const FH=(e,t,n)=>{switch(e.op){case"Equal":return[Qs(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[Mr(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[xs(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[er(R("a",e,t,n),R("b",e,t,n))];case"Less":return[xh(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[Br(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[Th(R("a",e,t,n))];case"LogicalOr":return[xp(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[Bn(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`)}},Pte="logical";const _H=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ct(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Ye(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[h,d]=R("args",e,t,n);return[Pp({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:h,activation:i,preluActivationWeights:d})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zte="matrices";const WH=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Wo(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[Wo(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[tw(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[Vo(R("x",e,t,n))];case"LogSoftmax":return[Ip(R("x",e,t,n))];case"SparseToDense":return[Lw(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`)}},Vte="normalization";const $H=(e,t,n)=>{switch(e.op){case"Max":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[es(R("x",e,t,n),s,i)]}case"Mean":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[jt(R("x",e,t,n),s,i)]}case"Min":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Ka(R("x",e,t,n),s,i)]}case"Sum":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[$e(R("x",e,t,n),s,i)]}case"All":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[lp(R("x",e,t,n),s,i)]}case"Any":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[mh(R("x",e,t,n),s,i)]}case"ArgMax":{const s=R("axis",e,t,n);return[fh(R("x",e,t,n),s)]}case"ArgMin":{const s=R("axis",e,t,n);return[$b(R("x",e,t,n),s)]}case"Prod":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[vp(R("x",e,t,n),s,i)]}case"Cumsum":{const s=R("axis",e,t,n),i=R("exclusive",e,t,n),o=R("reverse",e,t,n);return[yp(R("x",e,t,n),s,i,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gte="reduction";const UH=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{const s=R("n",e,t,n),i=R("axis",e,t,n);let o=R("tensors",e,t,n);return o=o.slice(0,s),[Ht(o,i)]}case"GatherV2":case"Gather":{const s=R("axis",e,t,n),i=R("x",e,t,n),o=R("indices",e,t,n);return[qa(i,Ae(o,"int32"),s)]}case"ReverseV2":case"Reverse":{const s=R("axis",e,t,n),i=R("x",e,t,n);return[Ts(i,s)]}case"Slice":{const s=R("begin",e,t,n),i=R("size",e,t,n);return[tt(R("x",e,t,n),s,i)]}case"StridedSlice":{const s=R("begin",e,t,n),i=R("end",e,t,n),o=R("strides",e,t,n),a=R("beginMask",e,t,n),c=R("endMask",e,t,n),h=R("ellipsisMask",e,t,n),d=R("newAxisMask",e,t,n),m=R("shrinkAxisMask",e,t,n),f=R("x",e,t,n);return[fw(f,s,i,o,a,c,h,d,m)]}case"Pack":return Q(()=>{const s=R("axis",e,t,n),i=R("tensors",e,t,n),o=i[0].shape,a=Pr(i[0]).shape,c=i.map(h=>{const d=ae(h.shape,o);if(!d&&!ae(Pr(h).shape,a))throw new Error("the input tensors shape does not match");return d?h:K(h,o)});return[ts(c,s)]});case"Unpack":{const s=R("axis",e,t,n),i=R("tensor",e,t,n);return ni(i,s)}case"Tile":{const s=R("reps",e,t,n);return[Ur(R("x",e,t,n),s)]}case"Split":case"SplitV":{const s=R("axis",e,t,n),i=R("numOrSizeSplits",e,t,n),o=R("x",e,t,n);return us(o,i,s)}case"ScatterNd":{const s=R("indices",e,t,n),i=R("values",e,t,n),o=R("shape",e,t,n);return[UA(s,i,o)]}case"GatherNd":{const s=R("x",e,t,n),i=R("indices",e,t,n);return[BA(s,i)]}case"SparseToDense":{const s=R("sparseIndices",e,t,n),i=R("outputShape",e,t,n),o=R("sparseValues",e,t,n),a=R("defaultValue",e,t,n);return[Lw(s,o,i,o.dtype===a.dtype?a:Ae(a,o.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yte="slice_join";const BH=(e,t,n)=>{switch(e.op){case"FFT":return[Oh(R("x",e,t,n))];case"IFFT":return[Qa(R("x",e,t,n))];case"RFFT":return[Eh(R("x",e,t,n))];case"IRFFT":return[kp(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Hte="spectral";const MH=(e,t,n)=>{switch(e.op){case"Cast":return[Ae(R("x",e,t,n),R("dtype",e,t,n))];case"ExpandDims":{const s=R("axis",e,t,n);return[Qn(R("x",e,t,n),s)]}case"Squeeze":{const s=R("axis",e,t,n);return[Pr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"MirrorPad":return[rw(R("x",e,t,n),R("padding",e,t,n),R("mode",e,t,n))];case"PadV2":case"Pad":return[Ni(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[vh(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[bh(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Kb(R("x",e,t,n),s,i)]}case"BroadcastTo":return[wh(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qte="transformation";function JN(e,t,n,s){const i=((o,a,c)=>{switch(o.category){case"arithmetic":return Q(()=>yH(o,a,c));case"basic_math":return Q(()=>bH(o,a,c));case"control":return AH(o,a,c);case"convolution":return Q(()=>vH(o,a,c));case"creation":return Q(()=>NH(o,a,c));case"dynamic":return CH(o,a,c);case"evaluation":return Q(()=>RH(o,a,c));case"image":return Q(()=>kH(o,a,c));case"graph":return Q(()=>OH(o,a,c));case"logical":return Q(()=>FH(o,a,c));case"matrices":return Q(()=>_H(o,a,c));case"normalization":return Q(()=>WH(o,a,c));case"reduction":return Q(()=>$H(o,a,c));case"slice_join":return Q(()=>UH(o,a,c));case"spectral":return Q(()=>BH(o,a,c));case"transformation":return Q(()=>MH(o,a,c));case"hash_table":return DH(o,a,c,s);case"custom":const h=YN(o.op);if(h&&h.customExecutor)return h.customExecutor(new gH(o,a,c));throw TypeError(`Custom op ${o.op} is not registered.`);default:throw TypeError(`Unknown op '${o.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 No(i)?i.then(o=>[].concat(o)):[].concat(i)}class ZN{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 QN(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>ps(b)[0]);let m=[];s!=null&&(m=s.map(b=>ps(b.name)[0]));const f=[...t];for(;f.length>0;){const b=f.pop();if((e0(b)||YH(b)||HH(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(d.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(h.has(w.name))return;h.add(w.name),f.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function PH(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>ps(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 h=new Set,d=[];for(;o.length>0;){const m=o.pop();h.add(m.name),t[m.name]||d.push(m),m.children.forEach(f=>{!h.has(f.name)&&s.has(f.name)&&f.inputs.every(b=>h.has(b.name))&&o.push(f)})}return d}const zH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],VH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],GH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function e0(e){return zH.indexOf(e.op)>=0}function YH(e){return VH.indexOf(e.op)>=0}function HH(e){return GH.indexOf(e.op)>=0}class DS{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 DS(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}set resourceManager(e){this._resourceManager=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=QN(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 [${o}]`);if(s.length>0){const a=t.map(h=>h.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return PH(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[ps(m)[0]]),i=t.map(m=>ps(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 h={},d={};return Q(()=>{const m=new ZN(this.weightMap,h,d,this.functionExecutorMap),f=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[x,v]=ps(L),N=[];N[v]=e[L],f[x]=N});const b=this.getFrozenTensorIds(f),w={};for(let L=0;L<c.length;L++){const x=c[L];if(!f[x.name]){const v=JN(x,f,m,this._resourceManager);if(No(v))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=v,this.checkTensorForDisposal(x.name,x,f,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>is(L,f,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 h=FY(c.name,n,s);h!=null&&h.forEach(d=>{if(d&&!i.has(d.id)){const m=a[d.id];m===1?(d.dispose(),delete a[d.id]):m!=null&&a[d.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 ZN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(f=>is(f,a,o)),h=c.map(f=>f.id),d=Object.keys(e).map(f=>e[f].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(f=>{const b=a[f];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(O=>this.graph.nodes[ps(O)[0]]),a=n.map(O=>ps(O)[0]);let c=a.map(O=>this.graph.nodes[O]);c.length===0&&(c=this._outputs);const{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:f}=QN(e,c,this.weightMap,this._initNodes),b=[...o,...this.graph.weights,...this._initNodes||[]].map(O=>({node:O,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(O=>{const[E,k]=ps(O),F=[];F[k]=e[O],w[E]=F});const L={},x=this.getFrozenTensorIds(w),v={};for(;b.length>0;){const O=this.processStack(o,b,t,w,v,x,a,L,h);await Promise.all(O)}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(O=>!e0(O)&&!is(O.name,w,t)).map(O=>O.name);if(N.length>0){let O="";throw m!=null&&(O=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${O}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let f="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([f]=cr(m.node.name,n)),s[m.node.name]==null){const b=JN(m.node,s,n,this._resourceManager);f||([f]=cr(m.node.name,n));const w=n.currentContext;No(b)?d.push(b.then(L=>(s[f]=L,n.currentContext=w,this.checkTensorForDisposal(f,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[f]=b,this.checkTensorForDisposal(f,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=cr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!is(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!is(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=ps(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);A(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&A(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 ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=ps(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ps(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}class qH{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}const jH="?tfjs-format=file",KH="model.json";class t0{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new qH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=tp(e,this.loadOptions);else{const t=yb(e,this.loadOptions);if(t.length===0)t.push(tp(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;const s=Jd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new DS(HN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null){const i=HN.Instance.transformGraph(e.modelInitializer);this.initializer=new DS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=gb(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 ee)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function XH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${KH}${jH}`));const n=new t0(e,t);return await n.load(),n}const n0="2.7.0";function JH(e,t){return Dm(e,t)}function Dm(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(mc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=Dm(c,t,n,s);o[a]=h}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 ZH(e,t=i0){return s0(e,t)}function s0(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(mc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=s0(c,t,n);o[a]=h}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function i0(e){return e===null?null:mc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function r0(e,t){const n=new Map;Dm(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(No(o)){const a=await o;n.set(i,a)}}const s=Dm(e,t,n);return s}function mc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof ee))}function QH(e){return e==null||eq(e)||Array.isArray(e)||typeof e=="object"&&e instanceof ee||hn(e)}function eq(e){return e===null||typeof e!="object"&&typeof e!="function"}function tq(e){return JH(e,nq)}function nq(e){return e instanceof ee?{value:e.clone(),recurse:!1}:mc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class o0{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 kS extends o0{constructor(){super(kS.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}}kS.INITIAL_CAPACITY=32;function a0(e){return new iq(e)}function jte(e){let t=e;return pu(()=>({value:t++,done:!1}))}function pu(e){return new rq(e)}function c0(e,t){return new h0(e,t)}function Kte(e,t,n){return c0(pu(e).take(t),n)}function sq(e,t=Zr.FAIL){return new mq(e,t)}class Sn{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 dq(this,e)}filter(e){return new hq(this,e)}map(e){return new uq(this,e)}mapAsync(e){return new l0(this,e)}serialMapAsync(e){return new l0(this,e).serial()}flatmap(e){return new pq(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 lq(this,e,t)}columnMajorBatch(e,t=!0,n=i0){const s=this.rowMajorBatch(e,t);return s.map(i=>ZH(i,n))}concatenate(e,t){return new h0(a0([this,e]),t)}take(e){return e<0||e==null?this:new cq(this,e)}skip(e){return e<0||e==null?this:new aq(this,e)}prefetch(e){return new u0(this,e)}shuffle(e,t){return new fq(this,e,t)}serial(){return new oq(this)}}class iq extends Sn{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:tq(e),done:!1}}}class rq extends Sn{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: ${e.message}`,e}}}class oq extends Sn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class aq extends Sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class cq extends Sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class lq extends Sn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class hq extends Sn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;He(e.value)}}}class uq extends Sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=this.transform(e.value),s=ji(n);for(const i of t)Kd(i,s)||i.dispose();return{value:n,done:!1}}}class dq extends Sn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 l0 extends Sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=await this.transform(e.value),s=ji(n);for(const i of t)Kd(i,s)||i.dispose();return{value:n,done:!1}}}class FS extends Sn{constructor(){super();this.outputQueue=new kS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class pq extends FS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=ji(e.value),n=this.transform(e.value),s=ji(n);this.outputQueue.pushAll(n);for(const i of t)Kd(i,s)||i.dispose();return!0}}class h0 extends Sn{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`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Zr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Zr||(Zr={}));class mq extends Sn{constructor(e,t=Zr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof Sn){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 r0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Zr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Zr.SHORTEST:return{value:null,done:!0};case Zr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class u0 extends Sn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new o0(t)}summary(){return`${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 fq extends u0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Za(n||Kn().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 fc{constructor(){this.size=null}batch(e,t=!0){const n=this;A(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ms(async()=>(await n.iterator()).columnMajorBatch(e,t,bq),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ms(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,ms(async()=>(await t.iterator()).filter(s=>Q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return ms(async()=>(await t.iterator()).map(n=>Q(()=>e(n))),this.size)}mapAsync(e){const t=this;return ms(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return ms(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,ms(async()=>{const s=pu(async()=>({value:await t.iterator(),done:!1}));return c0(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ms(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=Za(t||Kn().toString());return ms(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,ms(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()}}fc.MAX_BUFFER_SIZE=1e4;function ms(e,t=null){return new class extends fc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function gq(e){return ms(async()=>a0(e),e.length)}function yq(e){if(!mc(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 ms(async()=>{const n=await r0(e,s=>{if(s instanceof fc)return{value:s.iterator(),recurse:!1};if(mc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return sq(n,Zr.SHORTEST)},t)}function bq(e){if(e===null)return null;const t=e[0];if(QH(t)){const n=wq(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function wq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ee?ts(e):sn(e)}class d0 extends fc{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 km='"',mu=Symbol("out"),p0=Symbol("field"),Fm=Symbol("quote"),_S=Symbol("quoteafterquote"),m0=Symbol("quoteinquote");class f0 extends fc{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 d0(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(A(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&&A(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(A(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 h=null;if(c==="")if(a&&a.default!==void 0)h=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);h=void 0}else{const d=Number(c);if(isNaN(d))a&&a.dtype==="bool"?h=this.getBoolean(c):h=c;else if(!a||!a.dtype)h=d;else switch(a.dtype){case"float32":h=d;break;case"int32":h=Math.floor(d);break;case"bool":h=this.getBoolean(c);break;default:h=d}}a&&a.isLabel?s[o]=h:n[o]=h}}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=mu;for(let a=0;a<i;a++)switch(o){case mu:switch(e.charAt(a)){case km:s=a+1,o=Fm;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=mu;break;default:o=p0,s=a;break}break;case p0:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=mu,s=a+1;break;default:}break;case Fm:switch(e.charAt(a)){case km:o=_S;break;default:}break;case _S:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=mu,s=a+1;break;case km:o=Fm;break;default:o=m0;break}break;case m0:switch(e.charAt(a)){case km:o=Fm;break;default:}break;default:}if(o===_S?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 g0 extends Sn{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(oe().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new g0(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(P(t));return n.set(e,n.length-e.length),sn(n,t)}}class y0 extends Sn{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=hs([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=zr([o,i,c,a],[1,4])}else this.cropBox=zr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(oe().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 y0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&A(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: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$T(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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return Q(()=>{const t=e.toFloat().expandDims(0);let n;n=Vr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class b0{}class w0 extends Sn{split(e){return new Lq(this,e)}}class Lq extends w0{constructor(e,t){super();this.upstream=e,this.impl=new Sq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Sq extends FS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class Iq extends Sn{decodeUTF8(){return new xq(this)}}class xq extends w0{constructor(e){super();this.upstream=e,this.impl=new Tq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Tq extends FS{constructor(e){super();if(this.upstream=e,oe().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${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 oe().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class L0 extends Iq{constructor(e,t={}){super();this.file=e,this.options=t,A(e instanceof Uint8Array||(oe().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 Aq(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=vq(e));const i=await lT(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new L0(o,t)}else throw new Error(i.statusText)}const vq=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 S0(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class I0 extends b0{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(S0(this.input)&&oe().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new L0(this.input,this.options)}}class x0 extends b0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return S0(this.url)?new I0(this.url,this.fileOptions).iterator():Aq(this.url,this.fileOptions)}}function Nq(e,t={}){return new f0(new x0(e),t)}function Cq(e){const t=pu(e);return ms(async()=>t)}function Rq(e){return ms(async()=>{const t=await e();return pu(()=>t.next())})}async function Oq(e,t){return y0.create(e,t)}async function Eq(e){return g0.create(e)}const T0="2.7.0";var Dq=Object.freeze({__proto__:null,array:gq,Dataset:fc,zip:yq,CSVDataset:f0,TextLineDataset:d0,csv:Nq,func:Cq,generator:Rq,microphone:Eq,webcam:Oq,FileDataSource:I0,URLDataSource:x0,version_data:T0});function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&A(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const kq=zp,Fq=$w,_q=Uw,Wq=Bw,$q=_p;class Uq extends y{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new p(this,Ji())}write(e,t,n){this.firstUse&&(this.firstUse=!1,oe().get("IS_NODE")&&ic(`
============================
Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));const s={};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&qi(n[0])){const i=n.map(o=>Hd(o));s=this.write(i,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}incRef(e){const t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){const t=this.data.get(e);t.refCount--}}move(e,t,n,s){this.data.set(e,{values:t,dtype:s,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){const s=this.readSync(n.real.dataId),i=this.readSync(n.imag.dataId);return sr(s,i)}return this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if(e.dtype==="string")try{n=t.map(s=>rh(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(e.shape,e.dtype,n)}makeOutput(e,t,n){const s=this.write(e,t,n);return Ji().makeTensorFromDataId(s,t,n,this)}disposeData(e){if(this.data.has(e)){const{complexTensorInfos:t}=this.data.get(e);t!=null&&(this.disposeData(t.real.dataId),this.disposeData(t.imag.dataId)),this.data.delete(e)}}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.data.has(t)){const n=this.data.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}async time(e){const t=Kn();e();const n=Kn()-t;return{kernelMs:n}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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De=v[Ne];Ne=Oe+Se*N[2]+ue*N[1]+j*N[0];const Ue=v[Ne];Ne=Oe+we*N[2]+me*N[1]+j*N[0];const ze=v[Ne];Ne=Oe+Se*N[2]+me*N[1]+j*N[0];const ht=v[Ne],it=De+(Ue-De)*xe,rt=ze+(ht-ze)*xe;Ne=Oe+ye*O[2]+de*O[1]+E*O[0],w.values[Ne]=it+(rt-it)*ce}}}else for(let ue=0;ue<b;++ue){const me=b>1?U*(h-1)+ue*ie:.5*(U+Y)*(h-1);if(me<0||me>h-1){for(let pe=0;pe<d;pe++){const we=pe+ue*O[2]+de*O[1]+E*O[0];w.values[we]=o}continue}const ce=Math.round(me),ye=Math.round(he);for(let pe=0;pe<d;pe++){const we=pe+ce*N[2]+ye*N[1]+j*N[0],Se=pe+ue*O[2]+de*O[1]+E*O[0];w.values[Se]=v[we]}}}}return w.toTensor()}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,sliceSize:a,strides:c,outputSize:h}=Pa(t,e,n),d=!1;return this.scatter(e,t,n,h,a,o,i,c,s,d)}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=np(e,t);if(o===0)return sn([],i,e.dtype);const h=new an([o,a],e.dtype),d=this.readSync(t.dataId),m=this.readSync(e.dataId);for(let f=0;f<o;f++){const b=[];let w=0;for(let L=0;L<s;L++){const x=d[f*s+L];w+=x*c[L],b.push(x)}if(w<0||w>=e.size/a)throw new Error(`Invalid indices: ${b} does not index into ${e.shape}`);for(let L=0;L<a;L++)h.values[f*a+L]=m[w*a+L]}return h.toTensor().reshape(i)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=Pa(t,e,n),h=Ce(0),d=!0;return this.scatter(e,t,n,c,o,i,s,a,h,d)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return Ya(e.shape,1,e.dtype)}zerosLike(e){const t=ws(e.dtype,P(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,n){return _w(e,t,n)}scatter(e,t,n,s,i,o,a,c,h,d){const m=[s/i,i],f=this.readSync(e.dataId),b=this.readSync(t.dataId);if(s===0)return sn([],n,t.dtype);const w=new an(m,t.dtype);w.values.fill(this.readSync(h.dataId)[0]);for(let L=0;L<o;L++){const x=[];let v=0;for(let N=0;N<a;N++){const O=f[L*a+N];x.push(O),v+=O*c[N]}if(v<0||v>=s/i)throw new Error(`Invalid indices: ${x} does not index into ${n}`);for(let 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O=vo(N,c,h),E=O.slice(-f);x.forEach($=>E[$]=0);const k=Ws(E,f,w),F=O.slice(-b);v.forEach($=>F[$]=0);const U=Ws(F,b,L);m[N]=e(s[k],i[U])}return[m,a]}}function ui(e){const{inputs:t,backend:n}=e,{real:s,imag:i}=t,o=n.data.get(s.dataId).values,a=n.data.get(i.dataId).values,c=n.makeTensorInfo(s.shape,"complex64"),h=n.data.get(c.dataId);return h.complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",o),imag:n.makeTensorInfo(i.shape,"float32",a)},c}const Pq={kernelName:fd,backendName:"cpu",kernelFunc:ui};function Qo(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 zq={kernelName:kl,backendName:"cpu",kernelFunc:Qo};function fu(e){const{inputs:t,backend:n}=e,{input:s}=t,i=n.data.get(s.dataId).complexTensorInfos.real,o=n.data.get(i.dataId).values;return n.makeTensorInfo(i.shape,i.dtype,o)}const Vq={kernelName:Fd,backendName:"cpu",kernelFunc:fu};function gu(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return Qo({inputs:{x:i},backend:n});const a=dt(i.shape),c=gu({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=ui({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=fu({inputs:{input:i},backend:n}),c=gu({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!Ta(i.dtype,o)){const a=Qo({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32"){const a=n.data.get(i.dataId).values,c=Int32Array.from(a);return n.makeTensorInfo(i.shape,"int32",c)}if(o==="bool"){const a=n.data.get(i.dataId).values,c=Er([0],i.dtype),[h,d]=Qr((m,f)=>m!==f?1:0)(i.shape,[],a,c,"bool");return n.makeTensorInfo(d,"bool",h)}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const Gq={kernelName:Na,backendName:"cpu",kernelFunc:gu};function gc(e,t,n,s){return n==null?({inputs:i,backend:o})=>{const{a,b:c}=i,h=o;Te([a,c],e);const d=h.data.get(a.dataId).values,m=h.data.get(c.dataId).values,f=s||a.dtype,[b,w]=t(a.shape,c.shape,d,m,f);return h.makeTensorInfo(w,f,b)}:({inputs:i,backend:o})=>{const{a,b:c}=i,h=o;if(a.dtype==="complex64"||c.dtype==="complex64"){const d=gu({inputs:{x:a},backend:h,attrs:{dtype:"complex64"}}),m=h.data.get(d.dataId),f=m.complexTensorInfos.real,b=m.complexTensorInfos.imag,w=h.data.get(f.dataId).values,L=h.data.get(b.dataId).values,x=gu({inputs:{x:c},backend:h,attrs:{dtype:"complex64"}}),v=h.data.get(x.dataId),N=v.complexTensorInfos.real,O=v.complexTensorInfos.imag,E=h.data.get(N.dataId).values,k=h.data.get(O.dataId).values,[F,U,$]=n(a.shape,c.shape,w,L,E,k),Y=h.makeTensorInfo($,"float32",F),j=h.makeTensorInfo($,"float32",U),Z=ui({inputs:{real:Y,imag:j},backend:h});return h.disposeIntermediateTensorInfo(d),h.disposeIntermediateTensorInfo(x),h.disposeIntermediateTensorInfo(Y),h.disposeIntermediateTensorInfo(j),Z}else{const 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xt(e,t,n){return({inputs:s,attrs:i,backend:o})=>{const{x:a}=s;if(Te(a,e),a.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");const c=o,h=c.data.get(a.dataId).values,d=P(a.shape),m=n||a.dtype,f=ws(m,d);for(let b=0;b<d;++b)f[b]=t(h[b],i);return c.makeTensorInfo(a.shape,m,f)}}function bc(e,t,n){return({inputs:s,attrs:i,backend:o})=>{const{x:a}=s;if(Te(a,e),a.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");const c=o,h=c.data.get(a.dataId).values,d=n||a.dtype,m=t(h,d,i);return c.makeTensorInfo(a.shape,d,m)}}const N0=yc(e=>Math.ceil(e)),qq=bc(xl,N0),jq={kernelName:xl,backendName:"cpu",kernelFunc:qq};const C0=yc(e=>Math.exp(e)),Kq=bc(Rl,C0),Xq={kernelName:Rl,backendName:"cpu",kernelFunc:Kq};const R0=yc(e=>Math.expm1(e)),Jq=bc(Ol,R0),Zq={kernelName:Ol,backendName:"cpu",kernelFunc:Jq};const O0=yc(e=>Math.floor(e)),Qq=bc(El,O0),e4={kernelName:El,backendName:"cpu",kernelFunc:Qq};const 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b4={kernelName:kd,backendName:"cpu",kernelFunc:V0};const G0=xt(Gl,e=>Math.max(0,e)),w4={kernelName:Gl,backendName:"cpu",kernelFunc:G0};const Y0=xt(Hl,e=>Math.min(Math.max(0,e),6)),L4={kernelName:Hl,backendName:"cpu",kernelFunc:Y0};function BS(e,t,n,s){if(n==="linear")return Qo({inputs:{x:t},backend:e});if(n==="relu")return G0({inputs:{x:t},backend:e});if(n==="elu")return z0({inputs:{x:t},backend:e});if(n==="relu6")return Y0({inputs:{x:t},backend:e});if(n==="prelu")return V0({inputs:{x:t,alpha:s},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function ki(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=P(i.shape),c=Gt(o,a),h=P(c);A(a===h,()=>`The new shape (${c}) has ${h} elements and the old shape (${i.shape}) has ${a} elements. 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De=0;De<w;++De){const Ue=Ne+De*x;if(Ue<0||Ue>=b.inHeight)continue;const ze=De*U[0],ht=we+Ue*Y;for(let it=0;it<b.outWidth;++it){const rt=Oe+it*he,mt=it*b.strideWidth-N;for(let ut=0;ut<L;++ut){const Ft=mt+ut*v;if(Ft<0||Ft>=b.inWidth)continue;const rn=ze+ut*U[1],Ut=ht+Ft*j;let _t=rn;for(let Wt=0;Wt<b.inChannels;++Wt){const Jt=me[Ut+Wt*Z];for(let Rn=0;Rn<b.outChannels;++Rn)ye[rt+Rn*ue]+=Jt*ce[_t+Rn];_t+=b.outChannels}}}}}}return n.makeTensorInfo(k.shape,k.dtype,ye)}const q4={kernelName:gd,backendName:"cpu",kernelFunc:j0};function j4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,dy:o}=t,{strides:a,pad:c,dataFormat:h,dimRoundingMode:d,filterShape:m}=s;Te([i,o],"conv2dBackpropFilter");const f=$r(h),b=Fn(i.shape,m,a,1,c,d,!1,f),{strideHeight:w,strideWidth:L,filterHeight:x,filterWidth:v}=b,N=b.dataFormat==="channelsLast",O=new an(b.filterShape,"float32"),E=b.padInfo.left,k=b.padInfo.top,F=n.data.get(i.dataId).values,U=n.data.get(o.dataId).values,$=new an(i.shape,i.dtype,F),Y=new an(o.shape,o.dtype,U);for(let j=0;j<x;++j){const Z=Math.max(0,Math.ceil((k-j)/w)),ie=Math.min(b.outHeight,(b.inHeight+k-j)/w);for(let de=0;de<v;++de){const he=Math.max(0,Math.ceil((E-de)/L)),ue=Math.min(b.outWidth,(b.inWidth+E-de)/L);for(let me=0;me<b.inChannels;++me)for(let ce=0;ce<b.outChannels;++ce){let ye=0;for(let pe=0;pe<b.batchSize;++pe)for(let we=Z;we<ie;++we){const Se=j+we*w-k;for(let xe=he;xe<ue;++xe){const Oe=de+xe*L-E;N?ye+=$.get(pe,Se,Oe,me)*Y.get(pe,we,xe,ce):ye+=$.get(pe,me,Se,Oe)*Y.get(pe,ce,we,xe)}}O.set(ye,j,de,me,ce)}}}return n.makeTensorInfo(O.shape,O.dtype,O.values)}const K4={kernelName:Ty,backendName:"cpu",kernelFunc:j4};function X4(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,filter:o}=t,{inputShape:a,strides:c,pad:h,dataFormat:d,dimRoundingMode:m}=s;Te([i,o],"conv2dBackpropInput");const f=je(o.shape),b=je(i.shape);let w=$r(d);const L=Fn(a,o.shape,c,1,h,m,!1,w),x=new an(L.inShape,"float32"),v=x.values,N=n.data.get(i.dataId).values,O=n.data.get(o.dataId).values,[E,k,F]=f,{batchSize:U,filterHeight:$,filterWidth:Y,inChannels:j,inHeight:Z,inWidth:ie,outChannels:de,outHeight:he,outWidth:ue,strideHeight:me,strideWidth:ce}=L;w=L.dataFormat;const ye=$-1-L.padInfo.top,pe=Y-1-L.padInfo.left,we=w==="channelsLast",Se=x.strides[0],xe=we?x.strides[1]:x.strides[2],Oe=we?x.strides[2]:1,Ne=we?1:x.strides[1],De=b[0],Ue=we?b[1]:b[2],ze=we?b[2]:1,ht=we?1:b[1];for(let it=0;it<U;++it)for(let rt=0;rt<j;++rt)for(let mt=0;mt<Z;++mt){const ut=mt-ye,Ft=Math.max(0,Math.ceil(ut/me)),rn=Math.min(he,($+ut)/me);for(let Ut=0;Ut<ie;++Ut){const _t=Ut-pe,Wt=Math.max(0,Math.ceil(_t/ce)),Jt=Math.min(ue,(Y+_t)/ce);let Rn=0;for(let On=Ft;On<rn;++On){const di=On*me-ut;for(let Cs=Wt;Cs<Jt;++Cs){const na=Cs*ce-_t,pi=De*it+Ue*On+ze*Cs,_i=E*($-1-di)+k*(Y-1-na)+F*rt;for(let to=0;to<de;++to){const no=N[pi+ht*to],so=O[_i+to];Rn+=no*so}}}const fr=Se*it+xe*mt+Oe*Ut+Ne*rt;v[fr]=Rn}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}const J4={kernelName:yd,backendName:"cpu",kernelFunc:X4};function Z4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,filter:o}=t,{strides:a,pad:c,dilations:h}=s;Te([i,o],"conv3d");const d=_r(i.shape,o.shape,a,h,c),{filterDepth:m,filterHeight:f,filterWidth:b,dilationDepth:w,dilationHeight:L,dilationWidth:x,padInfo:v}=d,N=v.front,O=v.left,E=v.top,k=new an(d.outShape,i.dtype),F=n.data.get(i.dataId).values,U=n.data.get(o.dataId).values,$=k.values,Y=je(i.shape),j=je(o.shape);for(let Z=0;Z<d.batchSize;++Z){const ie=Z*Y[0],de=Z*k.strides[0];for(let he=0;he<d.outDepth;++he){const ue=de+he*k.strides[1],me=he*d.strideDepth-N;for(let ce=0;ce<m;++ce){const ye=me+ce*w;if(ye<0||ye>=d.inDepth)continue;const pe=ce*j[0],we=ie+ye*Y[1];for(let Se=0;Se<d.outHeight;++Se){const xe=ue+Se*k.strides[2],Oe=Se*d.strideHeight-E;for(let Ne=0;Ne<f;++Ne){const De=Oe+Ne*L;if(De<0||De>=d.inHeight)continue;const Ue=pe+Ne*j[1],ze=we+De*Y[2];for(let ht=0;ht<d.outWidth;++ht){const it=xe+ht*d.outChannels,rt=ht*d.strideWidth-O;for(let mt=0;mt<b;++mt){const ut=rt+mt*x;if(ut<0||ut>=d.inWidth)continue;const Ft=Ue+mt*j[2],rn=ze+ut*d.inChannels;let Ut=Ft;for(let _t=0;_t<d.inChannels;++_t){const Wt=F[rn+_t];for(let Jt=0;Jt<d.outChannels;++Jt)$[it+Jt]+=Wt*U[Ut+Jt];Ut+=d.outChannels}}}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}const Q4={kernelName:bd,backendName:"cpu",kernelFunc:Z4};function ej(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,dy:o}=t,{strides:a,pad:c,filterShape:h}=s;Te([i,o],"conv3dBackpropFilterV2");const d=je(i.shape),m=je(o.shape),f=_r(i.shape,h,a,1,c),b=f.strideDepth,w=f.strideHeight,L=f.strideWidth,x=f.filterDepth,v=f.filterHeight,N=f.filterWidth,O=new an(f.filterShape,"float32"),E=O.values,[k,F,U,$]=O.strides,Y=n.data.get(o.dataId).values,[j,Z,ie,de]=m,he=n.data.get(i.dataId).values,[ue,me,ce,ye]=d,pe=f.padInfo.front,we=f.padInfo.left,Se=f.padInfo.top;for(let xe=0;xe<x;++xe){const Oe=Math.max(0,Math.ceil((pe-xe)/b)),Ne=Math.min(f.outDepth,(f.inDepth+pe-xe)/b),De=xe*k;for(let Ue=0;Ue<v;++Ue){const ze=Math.max(0,Math.ceil((Se-Ue)/w)),ht=Math.min(f.outHeight,(f.inHeight+Se-Ue)/w),it=Ue*F+De;for(let rt=0;rt<N;++rt){const mt=Math.max(0,Math.ceil((we-rt)/L)),ut=Math.min(f.outWidth,(f.inWidth+we-rt)/L),Ft=rt*U+it;for(let rn=0;rn<f.inChannels;++rn){const Ut=rn*$+Ft;for(let _t=0;_t<f.outChannels;++_t){let Wt=0;for(let Jt=0;Jt<f.batchSize;++Jt){const Rn=Jt*ue,fr=Jt*j;for(let On=Oe;On<Ne;++On){const di=xe+On*b-pe,Cs=di*me+Rn,na=On*Z+fr;for(let pi=ze;pi<ht;++pi){const _i=Ue+pi*w-Se,to=_i*ce+Cs,no=pi*ie+na;for(let so=mt;so<ut;++so){const Oc=rt+so*L-we,sI=Oc*ye+to,iI=so*de+no;Wt+=he[sI+rn]*Y[iI+_t]}}}}E[Ut+_t]=Wt}}}}}return n.makeTensorInfo(O.shape,O.dtype,O.values)}const tj={kernelName:Ay,backendName:"cpu",kernelFunc:ej};function nj(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,filter:o}=t,{pad:a,strides:c,inputShape:h}=s;Te([i],"conv3dBackpropInputV2");const d=je(i.shape),m=je(o.shape),f=_r(h,o.shape,c,1,a),b=new an(f.inShape,"float32"),w=b.values,[L,x,v,N]=b.strides,O=n.data.get(i.dataId).values,[E,k,F,U]=d,$=n.data.get(o.dataId).values,[Y,j,Z,ie]=m,{batchSize:de,filterDepth:he,filterHeight:ue,filterWidth:me,inChannels:ce,inDepth:ye,inHeight:pe,inWidth:we,outChannels:Se,outDepth:xe,outHeight:Oe,outWidth:Ne,strideDepth:De,strideHeight:Ue,strideWidth:ze}=f,ht=he-1-f.padInfo.front,it=ue-1-f.padInfo.top,rt=me-1-f.padInfo.left;for(let mt=0;mt<de;++mt)for(let ut=0;ut<ce;++ut)for(let Ft=0;Ft<ye;++Ft){const rn=Ft-ht,Ut=Math.max(0,Math.ceil(rn/De)),_t=Math.min(xe,(he+rn)/De);for(let Wt=0;Wt<pe;++Wt){const Jt=Wt-it,Rn=Math.max(0,Math.ceil(Jt/Ue)),fr=Math.min(Oe,(ue+Jt)/Ue);for(let On=0;On<we;++On){const di=On-rt,Cs=Math.max(0,Math.ceil(di/ze)),na=Math.min(Ne,(me+di)/ze);let pi=0;for(let _i=Ut;_i<_t;++_i){const to=_i*De-rn;for(let no=Rn;no<fr;++no){const so=no*Ue-Jt;for(let Oc=Cs;Oc<na;++Oc){const sI=Oc*ze-di,iI=E*mt+k*_i+F*no+U*Oc,e9=Y*(he-1-to)+j*(ue-1-so)+Z*(me-1-sI)+ie*ut;for(let Km=0;Km<Se;++Km){const t9=O[iI+Km],n9=$[e9+Km];pi+=t9*n9}}}}w[L*mt+x*Ft+v*Wt+N*On+ut]=pi}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}const sj={kernelName:vy,backendName:"cpu",kernelFunc:nj};const ij=xt(Ca,e=>Math.cos(e)),rj={kernelName:Ca,backendName:"cpu",kernelFunc:ij};const oj=xt(vl,e=>Math.cosh(e)),aj={kernelName:vl,backendName:"cpu",kernelFunc:oj};function K0(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,filter:o}=t,{strides:a,pad:c,dilations:h,dimRoundingMode:d}=s;Te([i,o],"depthwiseConv2DNative");const m=je(i.shape),f=je(o.shape);let b=h;b==null&&(b=[1,1]),A(cn(a,b),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${b}'`);const w=Fn(i.shape,o.shape,a,b,c,d,!0),{filterHeight:L,filterWidth:x,dilationHeight:v,dilationWidth:N,padInfo:O}=w,E=O.left,k=O.top,F=w.outChannels/w.inChannels,U=new an(w.outShape,i.dtype),$=n.data.get(i.dataId).values,Y=n.data.get(o.dataId).values,j=U.values;for(let Z=0;Z<w.batchSize;++Z){const ie=Z*m[0],de=Z*U.strides[0];for(let he=0;he<w.outHeight;++he){const ue=de+he*U.strides[1],me=he*w.strideHeight-E;for(let ce=0;ce<L;++ce){const ye=me+ce*v;if(ye<0||ye>=w.inHeight)continue;const pe=ce*f[0],we=ie+ye*m[1];for(let Se=0;Se<w.outWidth;++Se){const xe=ue+Se*U.strides[2],Oe=Se*w.strideWidth-k;for(let Ne=0;Ne<x;++Ne){const De=Oe+Ne*N;if(De<0||De>=w.inWidth)continue;const Ue=pe+Ne*f[1],ze=we+De*w.inChannels;let ht=xe,it=Ue;for(let rt=0;rt<w.inChannels;++rt){const mt=$[ze+rt];for(let ut=0;ut<F;++ut)j[ht+ut]+=mt*Y[it+ut];ht+=F,it+=F}}}}}}return n.makeTensorInfo(U.shape,U.dtype,U.values)}const cj={kernelName:wd,backendName:"cpu",kernelFunc:K0};function lj(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,dy:o}=t,{strides:a,dilations:c,pad:h,dimRoundingMode:d,filterShape:m}=s;Te([i,o],"depthwiseConv2dNativeBackpropFilter");const f=Fn(i.shape,m,a,c,h,d,!0),{strideHeight:b,strideWidth:w,filterHeight:L,filterWidth:x}=f,v=new an(f.filterShape,"float32"),N=f.padInfo.left,O=f.padInfo.top,E=f.outChannels/f.inChannels,k=n.data.get(i.dataId).values,F=new an(i.shape,i.dtype,k),U=n.data.get(o.dataId).values,$=new an(o.shape,o.dtype,U);for(let Y=0;Y<L;++Y){const j=Math.max(0,Math.ceil((O-Y)/b)),Z=Math.min(f.outHeight,(f.inHeight+O-Y)/b);for(let ie=0;ie<x;++ie){const de=Math.max(0,Math.ceil((N-ie)/w)),he=Math.min(f.outWidth,(f.inWidth+N-ie)/w);for(let ue=0;ue<f.outChannels;++ue){const me=Math.trunc(ue/E),ce=ue%E;let ye=0;for(let pe=0;pe<f.batchSize;++pe)for(let we=j;we<Z;++we){const Se=Y+we*b-O;for(let xe=de;xe<he;++xe){const Oe=ie+xe*w-N;ye+=F.get(pe,Se,Oe,me)*$.get(pe,we,xe,ue)}}v.set(ye,Y,ie,me,ce)}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}const hj={kernelName:Cy,backendName:"cpu",kernelFunc:lj};function uj(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,filter:o}=t,{strides:a,dilations:c,pad:h,dimRoundingMode:d,inputShape:m}=s;Te([i,o],"depthwiseConv2DNativeBackpropInput");const f=je(i.shape),b=je(o.shape),w=Fn(m,o.shape,a,c,h,d,!0),L=new an(w.inShape,"float32"),x=L.values,[v,N,O]=L.strides,E=n.data.get(i.dataId).values,[k,F,U]=f,$=n.data.get(o.dataId).values,[Y,j,Z]=b,{batchSize:ie,filterHeight:de,filterWidth:he,inChannels:ue,inHeight:me,inWidth:ce,outChannels:ye,outHeight:pe,outWidth:we,strideHeight:Se,strideWidth:xe}=w,Oe=de-1-w.padInfo.top,Ne=he-1-w.padInfo.left,De=ye/ue;for(let Ue=0;Ue<ie;++Ue)for(let ze=0;ze<ue;++ze)for(let ht=0;ht<me;++ht){const it=ht-Oe,rt=Math.max(0,Math.ceil(it/Se)),mt=Math.min(pe,(de+it)/Se);for(let ut=0;ut<ce;++ut){const Ft=ut-Ne,rn=Math.max(0,Math.ceil(Ft/xe)),Ut=Math.min(we,(he+Ft)/xe);let _t=0;for(let Wt=rt;Wt<mt;++Wt){const Jt=Wt*Se-it;for(let Rn=rn;Rn<Ut;++Rn){const fr=Rn*xe-Ft,On=k*Ue+F*Wt+U*Rn,di=Y*(de-1-Jt)+j*(he-1-fr)+Z*ze;for(let Cs=0;Cs<De;++Cs){const na=ze*De+Cs,pi=E[On+na],_i=$[di+Cs];_t+=pi*_i}}}x[v*Ue+N*ht+O*ut+ze]=_t}}return n.makeTensorInfo(L.shape,L.dtype,L.values)}const dj={kernelName:Ry,backendName:"cpu",kernelFunc:uj};const pj={kernelName:Ld,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,f=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:x,inChannels:v,outHeight:N,outWidth:O,padInfo:E,strideHeight:k,strideWidth:F,filterHeight:U,filterWidth:$,dilationHeight:Y,dilationWidth:j,outShape:Z}=hp(s.shape,i.shape,o,a,"NHWC",c),ie=P(Z),de=Z.length,he=ws(s.dtype,ie);for(let me=0;me<w;++me)for(let ce=0;ce<N;++ce){const ye=ce*k-E.top;for(let pe=0;pe<O;++pe){const we=pe*F-E.left;for(let Se=0;Se<v;++Se){let xe=Number.MIN_SAFE_INTEGER;for(let Ne=0;Ne<U;++Ne){const De=ye+Ne*Y;if(De>=0&&De<L)for(let Ue=0;Ue<$;++Ue){const ze=we+Ue*j;if(ze>=0&&ze<x){const ht=Ws([me,De,ze,Se],m,je(s.shape)),it=Ws([Ne,Ue,Se],b,je(i.shape)),rt=d[ht]+f[it];rt>xe&&(xe=rt)}}}const Oe=Ws([me,ce,pe,Se],de,je(Z));he[Oe]=xe}}}const ue=h.write(Er(he,s.dtype),Z,s.dtype);return{dataId:ue,shape:Z,dtype:s.dtype}}};const mj={kernelName:Id,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:h}=n,d=t,m=Ls(s.shape,d.data.get(s.dataId).values),f=Ls(i.shape,d.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:L,inChannels:x,outHeight:v,outWidth:N,padInfo:O,strideHeight:E,strideWidth:k,filterHeight:F,filterWidth:U,dilationHeight:$,dilationWidth:Y,outShape:j}=hp(s.shape,i.shape,a,c,"NHWC",h);A(o.rank===j.length,()=>`Error in ${Id}, dy must have the same rank as output ${j.length}, but got ${o.rank}`);const Z=Ls(j,d.data.get(o.dataId).values),ie=fy(i.shape,i.dtype);for(let he=0;he<b;++he)for(let ue=0;ue<v;++ue){const me=ue*E-O.top;for(let ce=0;ce<N;++ce){const ye=ce*k-O.left;for(let pe=0;pe<x;++pe){let we=Number.MIN_SAFE_INTEGER,Se=0,xe=0;for(let Oe=0;Oe<F;++Oe){const Ne=me+Oe*$;if(Ne>=0&&Ne<w)for(let De=0;De<U;++De){const Ue=ye+De*Y;if(Ue>=0&&Ue<L){const ze=m[he][Ne][Ue][pe]+f[Oe][De][pe];ze>we&&(we=ze,Se=Oe,xe=De)}}}ie[Se][xe][pe]+=Z[he][ue][ce][pe]}}}const de=d.write(Er(ie,s.dtype),i.shape,i.dtype);return{dataId:de,shape:i.shape,dtype:i.dtype}}};const fj={kernelName:Sd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:h}=n,d=t,m=Ls(s.shape,d.data.get(s.dataId).values),f=Ls(i.shape,d.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:L,inChannels:x,outHeight:v,outWidth:N,padInfo:O,strideHeight:E,strideWidth:k,filterHeight:F,filterWidth:U,dilationHeight:$,dilationWidth:Y,outShape:j}=hp(s.shape,i.shape,a,c,"NHWC",h);A(o.rank===j.length,()=>`Error in ${Sd}, dy must have the same rank as output ${j.length}, but got ${o.rank}`);const Z=Ls(j,d.data.get(o.dataId).values),ie=fy(s.shape,s.dtype);for(let he=0;he<b;++he)for(let ue=0;ue<v;++ue){const me=ue*E-O.top;for(let ce=0;ce<N;++ce){const ye=ce*k-O.left;for(let pe=0;pe<x;++pe){let we=Number.MIN_SAFE_INTEGER,Se=me<0?0:me,xe=ye<0?0:ye;for(let Oe=0;Oe<F;++Oe){const Ne=me+Oe*$;if(Ne>=0&&Ne<w)for(let De=0;De<U;++De){const Ue=ye+De*Y;if(Ue>=0&&Ue<L){const ze=m[he][Ne][Ue][pe]+f[Oe][De][pe];ze>we&&(we=ze,Se=Ne,xe=Ue)}}}ie[he][Se][xe][pe]+=Z[he][ue][ce][pe]}}}const de=d.write(Er(ie,s.dtype),s.shape,s.dtype);return{dataId:de,shape:s.shape,dtype:s.dtype}}};const gj=Qr((e,t)=>e/t),yj=gc(Ra,gj),PS={kernelName:Ra,backendName:"cpu",kernelFunc:yj};const bj=Cw,wj=Rw,Lj=Ow,Sj=Ew,Ij=Dw,xj=kw,Tj=xt(Cl,e=>{const t=Math.sign(e),n=Math.abs(e),s=1/(1+bj*n);return t*(1-((((xj*s+Ij)*s+Sj)*s+Lj)*s+wj)*s*Math.exp(-n*n))}),Aj={kernelName:Cl,backendName:"cpu",kernelFunc:Tj};function X0(e,t,n){const 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int inIdx = ${c};
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}
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}
bvec4 isinf(vec4 val) {
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}
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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:h,defineRound:d}}function ea(e,t,n="index"){const s=je(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}; ${c};`}).join("")}function zm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function rne(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${zm(a)}, ${zm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${zm(o)}, ${zm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function XS(e){const t=je(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}const rC=`
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:oC}=Ww;function V5(e,t,n,s){const i=[];e.forEach(L=>{const x=P(L.shapeInfo.logicalShape);L.shapeInfo.isUniform?i.push(`uniform float ${L.name}${x>1?`[${x}]`:""};`):(i.push(`uniform sampler2D ${L.name};`),i.push(`uniform int offset${L.name};`))});const o=i.join(`
`),a=e.map(L=>G5(L,t,s)).join(`
`),c=t.texShape,h=Pn(),d=q5(h);let m,f,b=X5(h);t.isPacked?(m=Y5(t.logicalShape,c),f=K5(h)):(m=H5(t.logicalShape,c),f=j5(h)),s&&(b+=e8);const w=[b,d,f,o,m,a,n].join(`
`);return w}function Ic(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return d8(e);case 1:return m8(e);case 2:return g8(e);case 3:return b8(e);case 4:return L8(e);case 5:return S8(e);case 6:return I8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function aC(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return u8(e);case 1:return p8(e);case 2:return f8(e);case 3:return y8(e);default:return w8(e)}}function G5(e,t,n=!1){let s="";n?s+=aC(e):s+=Ic(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=x8(e,t):s+=T8(e,t)),s}function Y5(e,t){switch(e.length){case 0:return cC();case 1:return t8(e,t);case 2:return l8(e,t);case 3:return s8(e,t);default:return r8(e,t)}}function H5(e,t){switch(e.length){case 0:return cC();case 1:return n8(e,t);case 2:return h8(e,t);case 3:return i8(e,t);case 4:return o8(e,t);case 5:return a8(e,t);case 6:return c8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function q5(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function j5(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function K5(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function X5(e){const t=`${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);
}
${J5}
${Z5}
${Q5}
`;return t}const J5=`
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);
}
`,Z5=`
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);
}
`,Q5=`
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);
}
`,e8=`
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 cC(){return`
int getOutputCoords() {
return 0;
}
`}function t8(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 n8(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 * ${t[1]} + resTexRC.y;
}
`}function s8(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),i=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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, ${s}) * 2;
return ivec3(b, r, c);
}
`}function i8(e,t){const n=ea(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function r8(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),i=s*Math.ceil(e[e.length-2]/2);let o=i,a="",c="b, r, c";for(let h=2;h<e.length-1;h++)o*=e[e.length-h-1],a=`
int b${h} = index / ${o};
index -= b${h} * ${o};
`+a,c=`b${h}, `+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}(${c});
}
`}function o8(e,t){const n=ea(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function a8(e,t){const n=ea(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function c8(e,t){const n=ea(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function l8(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(ae(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${s}) * 2;
return ivec2(r, c);
}
`}function h8(e,t){return ae(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function ta(e){return`offset${e}`}function u8(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Pn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function d8(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=ta(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${o}, ${a}, ${c});
return sampleTexture(${t}, uv);
}
`}function p8(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=Pn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${o.texture2D}(${t}, uv);
}
`}function m8(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${xc(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=ta(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 f8(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=Pn();if(i!=null&&ae(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 h=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],d=Math.ceil(t[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${d}, ${h[0]}, ${h[1]}, row, col);
return ${c.texture2D}(${n}, uv);
}
`}function g8(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&&ae(t,i)){const f=i[0],b=i[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${f}.0);
return sampleTexture(${n}, uv);
}
`}const{newShape:o,keptDims:a}=ln(t),c=o;if(c.length<t.length){const f=Tc(e,c),b=["row","col"];return`
${Ic(f)}
float ${s}(int row, int col) {
return ${s}(${Ac(b,a)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${xc(e)}
}
`;const h=i[0],d=i[1],m=ta(n);return d===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) / ${h}.0);
return sampleTexture(${n}, uv);
}
`:h===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) / ${d}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${m};
vec2 uv = uvFromFlat(${h}, ${d}, index);
return sampleTexture(${n}, uv);
}
`}function y8(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 f=t.slice(1),b=[1,2],w=Tc(e,f),L=["b","row","col"];return`
${aC(w)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Ac(L,b)});
}
`}const a=o[0],c=o[1],h=Math.ceil(t[2]/2),d=h*Math.ceil(t[1]/2),m=Pn();return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${a}, ${c}, ${d}, ${h}, b, row, col);
return ${m.texture2D}(${n}, uv);
}
`}function b8(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}=ln(t),h=a;if(h.length<t.length){const L=Tc(e,h),x=["row","col","depth"];return`
${Ic(L)}
float ${s}(int row, int col, int depth) {
return ${s}(${Ac(x,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)));
${xc(e)}
}
`;const d=e.shapeInfo.texShape,m=d[0],f=d[1],b=e.shapeInfo.flatOffset;if(f===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(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(f===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(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;const w=ta(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}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function w8(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],h=a[1],d=Math.ceil(t[n-1]/2);let m=d*Math.ceil(t[n-2]/2),f="int b, int row, int col",b=`b * ${m} + (row / 2) * ${d} + (col / 2)`;for(let L=2;L<n-1;L++)f=`int b${L}, `+f,m*=t[n-L-1],b=`b${L} * ${m} + `+b;const w=Pn();return`
vec4 ${i}(${f}) {
int index = ${b};
int texR = index / ${h};
int texC = index - texR * ${h};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}, ${c});
return ${w.texture2D}(${s}, uv);
}
`}function L8(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:h}=ln(t);if(c.length<t.length){const L=Tc(e,c),x=["row","col","depth","depth2"];return`
${Ic(L)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Ac(x,h)});
}
`}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)));
${xc(e)}
}
`;const d=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,f=m[0],b=m[1];if(b===a&&d==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, ${f}.0);
return sampleTexture(${n}, uv);
}
`;if(b===i&&d==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, ${f}.0);
return sampleTexture(${n}, uv);
}
`;const w=ta(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 * ${i} + depth2;
vec2 uv = uvFromFlat(${f}, ${b}, index + ${w});
return sampleTexture(${n}, uv);
}
`}function S8(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:h,keptDims:d}=ln(t);if(h.length<t.length){const x=Tc(e,h),v=["row","col","depth","depth2","depth3"];return`
${Ic(x)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${Ac(v,d)});
}
`}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;
${xc(e)}
}
`;const m=e.shapeInfo.flatOffset,f=e.shapeInfo.texShape,b=f[0],w=f[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 L=ta(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 + ${L};
vec2 uv = uvFromFlat(${b}, ${w}, index);
return sampleTexture(${n}, uv);
}
`}function I8(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=ln(t);if(i.length<t.length){const v=Tc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
${Ic(v)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${Ac(N,o)});
}
`}const a=t[5],c=t[4]*a,h=t[3]*c,d=t[2]*h,m=t[1]*d;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}, ${d}, ${h}, ${c})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${xc(e)}
}
`;const f=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],L=b[1];if(L===m&&f==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${d}, ${h}, ${c}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${L}.0, ${w}.0);
return sampleTexture(${n}, uv);
}
`;if(L===a&&f==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]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${L}.0, ${w}.0);
return sampleTexture(${n}, uv);
}
`;const x=ta(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 * ${d} + depth * ${h} +
depth2 * ${c} + depth3 * ${a} + depth4 + ${x};
vec2 uv = uvFromFlat(${w}, ${L}, index);
return sampleTexture(${n}, uv);
}
`}function xc(e){const t=e.name,n=P(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function x8(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,c=oC(e.shapeInfo.logicalShape,t.logicalShape),h=Rt(a),d=a-o;let m;const f=["x","y","z","w","u","v"];o===0?m="":a<2&&c.length>=1?m="coords = 0;":m=c.map(O=>`coords.${f[O+d]} = 0;`).join(`
`);let b="";a<2&&o>0?b="coords":b=e.shapeInfo.logicalShape.map((O,E)=>`coords.${f[E+d]}`).join(", ");let w="return outputValue;";const L=P(e.shapeInfo.logicalShape),x=L===1,v=P(t.logicalShape),N=v===1;if(o===1&&!x&&!N)w=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(x&&!N)a===1?w=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:w=`
return vec4(outputValue.x);
`;else if(c.length){const O=o-2,E=o-1;c.indexOf(O)>-1&&c.indexOf(E)>-1?w="return vec4(outputValue.x);":c.indexOf(O)>-1?w="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":c.indexOf(E)>-1&&(w="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${i}() {
${h} coords = getOutputCoords();
${m}
vec4 outputValue = get${s}(${b});
${w}
}
`}function T8(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,h=t.logicalShape.length;if(!e.shapeInfo.isUniform&&c===h&&e.shapeInfo.flatOffset==null&&ae(a,o))return`
float ${i}() {
return sampleTexture(${n}, resultUV);
}
`;const d=Rt(h),m=oC(e.shapeInfo.logicalShape,t.logicalShape),f=h-c;let b;const w=["x","y","z","w","u","v"];c===0?b="":h<2&&m.length>=1?b="coords = 0;":b=m.map(x=>`coords.${w[x+f]} = 0;`).join(`
`);let L="";return h<2&&c>0?L="coords":L=e.shapeInfo.logicalShape.map((x,v)=>`coords.${w[v+f]}`).join(", "),`
float ${i}() {
${d} coords = getOutputCoords();
${b}
return get${s}(${L});
}
`}function Rt(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 ${e} is not yet supported`)}function Tc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Ac(e,t){return t.map(n=>e[n]).join(", ")}class A8{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,A(e.length>2,()=>`Packed arg${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,h=Rt(c),d=Mn("coords",c);let m,f;if(o===1){f=c+1;const $=Rt(f);m=`
${$} sourceLocR = ${$}(${d.join()}, 0);
++${d[c-1]};
${$} sourceLocG = ${$}(${d.join()}, 0);
++${d[c-2]};
${$} sourceLocA = ${$}(${d.join()}, 0);
--${d[c-1]};
${$} sourceLocB = ${$}(${d.join()}, 0);
--${d[c-2]};`}else f=c,m=`
${h} sourceLocR = coords;
++${d[c-1]};
${h} sourceLocG = coords;
++${d[c-2]};
${h} sourceLocA = coords;
--${d[c-1]};
${h} sourceLocB = coords;
--${d[c-2]};`;const b=["x","y","z","w","u","v"].slice(0,f),w="."+b[f-1],L=b.map($=>"int "+$),x=Mn("sourceLocR",f-1).concat("inIdx.r"),v=Mn("sourceLocG",f-1).concat("inIdx.g"),N=Mn("sourceLocB",f-1).concat("inIdx.b"),O=Mn("sourceLocA",f-1).concat("inIdx.a"),E=n==="max"?"greaterThan":"lessThan",k=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${v.join()}),
getBestIndicesAChannel(${N.join()}),
getBestIndicesAChannel(${O.join()})));`,F=`vec4(
getAChannel(${x.join()}),
hasNextCol ? getAChannel(${v.join()}) : 0.,
hasNextRow ? getAChannel(${N.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${O.join()}) : 0.)`,U=s?"":`
float getBestIndicesAChannel(${L.join()}) {
return getChannel(getBestIndicesA(${b.join()}),
vec2(${b.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${L.join()}) {
return getChannel(getA(${b.join()}),
vec2(${b.slice(-2).join()}));
}
${U}
void main() {
${h} coords = getOutputCoords();
bool hasNextCol = ${d[c-1]} < ${a[c-1]-1};
bool hasNextRow = ${d[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 = ${F};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${F};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${E}(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 v8{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,h=e.effectiveFilterWidth,d=c-1-e.padInfo.top,m=h-1-e.padInfo.left,f=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${d}, ${m});
const float avgMultiplier = float(${f});
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 < ${h};
wC+= ${a}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}}class N8{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.effectiveFilterDepth,f=e.effectiveFilterHeight,b=e.effectiveFilterWidth,w=m-1-e.padInfo.front,L=f-1-e.padInfo.top,x=b-1-e.padInfo.left,v=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${w}, ${L}, ${x});
const float avgMultiplier = float(${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 < ${f};
wR += ${h}) {
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 += ${d}) {
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 lC=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,C8=`
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;
}
`,R8=`
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);
`,one="return (a - b) * (a - b);",O8="return float(a == b);",E8="return float(a < b);",D8="return float(a <= b);",k8="return float(a > b);",F8="return float(a >= b);",_8="return float(a >= 1.0 && b >= 1.0);",W8="return float(a >= 1.0 || b >= 1.0);",$8=lC+`
return max(a, b);
`,U8=lC+`
return min(a, b);
`,B8=`if (b == 0.0) return NAN;
return mod(a, b);`,M8="return (b >= 1.0) ? a : a * (b + 1.0);",hC="return (a < 0.) ? b * a : a;";class Wn{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 Vm=`
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;
`,P8=`
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);
`,z8=`
// 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));
`+Vm+`
return result;
`,uC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,V8=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,G8=`
return vec4(equal(a, b));
`,ane=`
return vec4(notEqual(a, b));
`,Y8=`
return vec4(lessThan(a, b));
`,H8=`
return vec4(lessThanEqual(a, b));
`,q8=`
return vec4(greaterThan(a, b));
`,j8=`
return vec4(greaterThanEqual(a, b));
`,K8=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,X8=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,J8=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Vm+`
return result;
`,Z8=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Vm+`
return result;
`,Q8=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Vm+`
return result;
`;class ur{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||P(this.outputShape)===1)o=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else{const a=Rt(i);if(o=`
${a} coords = getOutputCoords();
`,i===1)o+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{const c=Mn("coords",i);o+=`
bool nextRowOutOfBounds =
(${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 e6{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 t6{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 n6{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 s6{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 i6{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,h=o?1:2,d=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[${h}], coords[${d}]) - 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 r6{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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class o6{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=t-1-e.padInfo.front,h=n-1-e.padInfo.top,d=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${c}, ${h}, ${d});
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 a6{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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}}class c6{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.top,a=n-1-e.padInfo.left,c=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 dC{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,h=e.dilationHeight,d=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,b=Math.floor(e.inChannels/4)*4,w=e.inChannels%4,L=e.dataFormat==="channelsLast",x=L?1:2,v=L?2:3,N=L?3:1;let O="",E="";n&&(s?O=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:O=`
float activation(float x) {
${n}
}
`,E="result = activation(result);");const k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${O}
const ivec2 strides = ivec2(${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[${x}], coords[${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 * ${h};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${b}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${L}) {
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 (${w===1}) {
if (${L}) {
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, ${b} + 1, d2)
);
if (${L}) {
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, ${b} + 2, d2)
);
if (${L}) {
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;
${k}
${E}
setOutput(result);
}
`}}class l6{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.filterDepth,f=e.filterHeight,b=e.filterWidth,w=Math.floor(e.inChannels/4)*4,L=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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 < ${f}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${w}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${L===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${w}) *
getW(wF, wR, wC, ${w}, d2);
} else if (${L===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, ${w} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${L===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 pC{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,h=e.strideHeight,d=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=e.outChannels/e.inChannels;let x="",v="";n&&(s?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:x=`
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=`
${x}
const ivec2 strides = ivec2(${h}, ${d});
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${L};
int q = d2 - d1 * ${L};
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 * ${f};
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}
${v}
setOutput(result);
}
`}}class mC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,h=e.strideHeight,d=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=w;let x="int xR; int xC; int xCOffset;";for(let E=0;E<b;E++)for(let k=0;k<w;k++)x+=`
vec4 xTexelR${E}C${k*2} = vec4(0.);
vec4 wR${E}C${k} = vec4(0.);
vec4 xR${E}C${k} = vec4(0.);`;for(let E=0;E<b;E++)for(let k=0;k<L;k++){const F=k*2;if(x+=`
xR = xRCorner + ${E*m};
xC = xCCorner + ${F*f};
`,d===1){if(F<w&&(c%2===1?x+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${o}) {
xTexelR${E}C${F}.zw = vec2(0.);
}
} else {
xTexelR${E}C${F} = 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 >= ${o}) {
previous.zw = vec2(0.);
}
xR${E}C${F} = vec4(previous.zw, xTexelR${E}C${F}.xy);
} else {
xR${E}C${F} = vec4(0, 0, xTexelR${E}C${F}.xy);
}
`:x+=`
if(xR >= 0 && xR < ${i} && xC >= 0 && xC < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xC, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
xR${E}C${F} = xTexelR${E}C${F};
`,F+1<w)){const U=c%2===0?T(f):f;f%2===0&&c%2===1||f%2!==0&&c%2!==1?(x+=`
xCOffset = xC + ${c%2} + ${U};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xCOffset, d1);
}
`,f>1&&(x+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
`),x+=`
xR${E}C${F+1} = vec4(
xTexelR${E}C${F}.zw, xTexelR${E}C${F+2}.xy);
`):x+=`
xCOffset = xC + ${U};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xCOffset, d1);
}
xR${E}C${F+1} = xTexelR${E}C${F+2};
`}}else F<w&&(x+=`
if(xR >= 0 && xR < ${i}) {
`,c%2===1?(x+=`
xCOffset = xC + 1 - ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${E}C${F+2} = vec4(0.);
}
xR${E}C${F} = vec4(
xTexelR${E}C${F}.zw, xTexelR${E}C${F+2}.zw);
`,F+1<w&&(x+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${E}C${F+1} = vec4(xTexelR${E}C${F+2}.xy, final.xy);
`)):(x+=`
if(xC >= 0 && xC < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xC, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
xCOffset = xC + ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${F+2} = vec4(0.);
}
xR${E}C${F} = vec4(
xTexelR${E}C${F}.xy, xTexelR${E}C${F+2}.xy);
`,F+1<w&&(x+=`
xR${E}C${F+1} = vec4(
xTexelR${E}C${F}.zw, xTexelR${E}C${F+2}.zw);
`)),x+="}");F<w&&(x+=`
vec4 wTexelR${E}C${F} = getW(${E}, ${F}, d1, q);
wR${E}C${F} = vec4(wTexelR${E}C${F}.xz, wTexelR${E}C${F}.xz);
`,F+1<w&&(x+=`
vec4 wTexelR${E}C${F+1} = getW(${E}, ${F+1}, d1, q);
wR${E}C${F+1} =
vec4(wTexelR${E}C${F+1}.xz, wTexelR${E}C${F+1}.xz);`))}for(let E=0;E<b;E++)for(let k=0;k<w;k++)x+=`dotProd += xR${E}C${k} * wR${E}C${k};`;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 O=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${v}
const ivec2 strides = ivec2(${h}, ${d});
const ivec2 pads = ivec2(${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.);
${x}
vec4 result = dotProd;
${O}
${N}
setOutput(result);
}
`}}class h6{constructor(e,t,n,s,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[o,a,c,h]=e,[d]=t,[m,f]=n;this.outputShape=[d,m,f,h];const b=s==="bilinear"?1:0,[w,L]=[`${a-1}.0`,`${c-1}.0`],[x,v,N]=m>1?[`${(a-1)/(m-1)}`,"(y2-y1) * height_ratio",`y1*${w} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${w}`],[O,E,k]=f>1?[`${(c-1)/(f-1)}`,"(x2-x1) * width_ratio",`x1*${L} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${L}`];this.userCode=`
const float height_ratio = float(${x});
const float width_ratio = float(${O});
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 = ${E};
float in_y = ${N};
if( in_y < 0.0 || in_y > ${w} ) {
setOutput(float(${i}));
return;
}
float in_x = ${k};
if( in_x < 0.0 || in_x > ${L} ) {
setOutput(float(${i}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${b} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}}class fC{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const s=e.length,i=t?"0.0":`getX(${gC(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() {
${Rt(s)} coords = getOutputCoords();
int end = ${yC(s,"coords")};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${a}) {
int idx = ${c};
${yC(s,"coords")} = idx;
val += getX(${gC(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 gC(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 ${e} is not yet supported`)}function yC(e,t){if(e===1)return`${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 u6{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=wu.DENSE;const t=Su(e),n=Pn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${ea(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 d6{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=wu.DENSE;const t=Su(e),n=Pn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${ea(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 p6{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 m6{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 f6{constructor(e){this.variableNames=["A"],this.outTexUsage=Ns.DOWNLOAD;const t=Pn();this.outputShape=e,this.userCode=`
${rC}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}}class g6{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ns.DOWNLOAD;const t=Pn();this.outputShape=e,this.userCode=`
${rC}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}}class y6{constructor(e,t,n=!1){this.variableNames=["A"];const s=Pn(),[i,o]=t;this.outputShape=e;let a="result";n&&(a="floor(result * 255. + 0.5)"),this.userCode=`
${XS(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 b6{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const s=Pn(),[i,o]=t;this.outputShape=e;let a="",c="result";n&&(c="floor(result * 255. + 0.5)");for(let h=0;h<=1;h++)for(let d=0;d<=1;d++){const m=h*2+d;a+=`
localCoords = coords;
if(localCoords[2] + ${d} < ${e[2]}) {
localCoords[2] += ${d};
if(localCoords[1] + ${h} < ${e[1]}) {
localCoords[1] += ${h};
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=`
${XS(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};
}
`}}class w6{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 L6{constructor(e,t,n){this.variableNames=["A","indices"];const s=e.slice();s[n]=t,this.outputShape=s,this.rank=s.length;const i=Rt(this.rank),o=S6(e,n);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}}function S6(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 I6{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;const s=Rt(t.length),i=Rt(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 * ${o};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}}function x6(e){const t=Pn(),n=`${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 n5(e,n)}function T6(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 c5(e,t)}function A6(e){const t=new Uint16Array([0,1,2,2,1,3]);return l5(e,t)}function xu(e,t,n,s,i,o){u5(t,n);const a=h5(e),c=e.TEXTURE_2D;return Ee(e,()=>e.bindTexture(c,a)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ee(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Ee(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function bC(e){return e.internalFormatFloat}function v6(e,t,n,s){const[i,o]=Lu(t,n);return xu(e,i,o,bC(s),s.textureFormatFloat,e.FLOAT)}function wC(e){return e.internalFormatHalfFloat}function N6(e,t,n,s){const[i,o]=Lu(t,n);return xu(e,i,o,wC(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function LC(e){return e.downloadTextureFormat}function C6(e,t,n,s){const[i,o]=Lu(t,n);return xu(e,i,o,LC(s),e.RGBA,e.UNSIGNED_BYTE)}function SC(e){return e.internalFormatPackedFloat}function R6(e,t,n,s){const[i,o]=wc(t,n);return xu(e,i,o,SC(s),e.RGBA,e.FLOAT)}function IC(e){return e.internalFormatPackedHalfFloat}function O6(e,t,n,s){const[i,o]=wc(t,n);return xu(e,i,o,IC(s),e.RGBA,s.textureTypeHalfFloat)}function E6(e,t,n){const s=0,i=3*4,o=3*4+2*4;Ee(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=eC(e,t,"clipSpacePos",n,3,o,s);return a&&eC(e,t,"uv",n,2,o,i)}function D6(e,t,n,s,i,o){Ee(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,h;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,h=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,h=o.internalFormatPackedFloat),a.set(i),Ee(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Ee(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function k6(e,t,n){Ee(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ee(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ee(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ee(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function F6(e,t,n,s){const i=e.createBuffer();Ee(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Ee(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Ee(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ee(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function _6(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 W6(e,t,n,s){const[i,o]=Lu(t,n),a=4,c=new Uint8Array(KK(t*n,a));return Ee(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function $6(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(XK(o,a));return h.bindBuffer(h.PIXEL_PACK_BUFFER,t),h.getBufferSubData(h.PIXEL_PACK_BUFFER,0,d),h.bindBuffer(h.PIXEL_PACK_BUFFER,null),d}function U6(e,t,n){const s=new Float32Array(t*n*4);return Ee(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class B6{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=oe().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,HK(t,e)):this.gl=Fi(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(oe().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=Wm(this.gl,i),Gs(this.gl,o))this.textureHalfFloatExtension=Wm(this.gl,o);else if(oe().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),Gs(this.gl,s))this.colorBufferHalfFloatExtension=Wm(this.gl,s);else if(oe().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",Gs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Gs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=T6(this.gl),this.indexBuffer=A6(this.gl),this.framebuffer=d5(this.gl),this.textureConfig=GS(this.gl,this.textureHalfFloatExtension)}get debug(){return oe().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;Ee(e,()=>e.finish()),Ee(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ee(e,()=>e.deleteFramebuffer(this.framebuffer)),Ee(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ee(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ee(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),v6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),N6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),C6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),k6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),D6(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),O6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),R6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(tC(this.gl,this.framebuffer),this.outputTexture=null),Ee(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>W6(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return $6(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return _6(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=F6(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(oe().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 oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>U6(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=s5(t,e),s=x6(t),i=o5(t);return Ee(t,()=>t.attachShader(i,s)),Ee(t,()=>t.attachShader(i,n)),a5(t,i),this.debug&&YS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=E6(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ee(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&YS(this.gl,this.program),Ee(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?m5(this.gl,e,t):f5(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ee(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(),g5(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=wc(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&&YS(this.gl,this.program),$m(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Ee(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Wm(this.gl,oe().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(oe().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(oe().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 $t(()=>this.disposed||this.isQueryAvailable(e,oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=M6(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;$t(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),HS(this.gl,e,this.framebuffer),this.debug&&$m(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(HS(this.gl,this.outputTexture,this.framebuffer),this.debug&&$m(this.gl)):tC(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;HS(s,e,this.framebuffer),this.debug&&$m(s),this.outputTexture=e,Ee(s,()=>s.viewport(0,0,t,n)),Ee(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function M6(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function P6(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const x={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(x.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:x}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=V5(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const f=e.getUniformLocation(d,"NAN",!1);oe().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(d,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const L=t.variableNames[w],x=!1;b[L]=e.getUniformLocation(d,L,x),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,x)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:f}}function xC(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(!ae(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,h=o.isUniform?null:o.texData.texShape;if(!ae(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function z6(e,t,n,s,i){xC(t.inShapeInfos,n),xC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),oe().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],f=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(P(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&&f!=null&&e.gl.uniform1i(f,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function V6(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,h=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${h}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class G6{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:h,dilationWidth:d,dilationHeight:m,dataFormat:f}=n,{left:b,top:w}=c,L=i*s,x=Pn(),v=f==="channelsLast",N=v?0:1,O=v?1:2;let E="";for(let k=0;k<=1;k++)for(let F=0;F<=1;F++)E+=`
blockIndex = rc.y + ${F};
pos = rc.x + ${k};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${h})) * ${a} - ${w};
d0 = offsetY + ${m} * (pos / ${L});
if(d0 < ${t[N]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${h}.) * ${o}. - ${b}.);
d1 = offsetX + ${d} * (int(mod(float(pos), ${L}.) / ${i}.));
if(d1 < ${t[O]} && d1 >= 0) {
ch = int(mod(float(pos), ${i}.));
if (${v}) {
innerDims = vec2(d1, ch);
result[${k*2+F}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${k*2+F}] = 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;
${E}
${x.output} = result;
}
`}}class Y6{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[];const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${n}) + float(${s}) * sum`;i===.5?c=`inversesqrt(${h})`:i===1?c=`1.0/(${h})`:c=`exp(log(${h}) * 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 H6{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 * ${i});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}}class q6{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${n}) + float(${s}) * sum`;i===.5?c=`inversesqrt(${h})`:i===1?c=`1.0/(${h})`:c=`exp(log(${h}) * 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 * ${c};
setOutput(result);
}
`}}class j6{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,i=e.effectiveFilterHeight,o=e.effectiveFilterWidth,a=i-1-e.padInfo.top,c=o-1-e.padInfo.left,h=i*o-1;this.userCode=`
const ivec2 pads = ivec2(${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 = ${h} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${o} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}}class K6{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,i=e.dilationDepth,o=e.dilationHeight,a=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,m=c-1-e.padInfo.front,f=h-1-e.padInfo.top,b=d-1-e.padInfo.left,w=c*h*d-1;this.userCode=`
const ivec3 pads = ivec3(${m}, ${f}, ${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 < ${h};
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 < ${d};
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 * ${h} * ${d} +
wR * ${d} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}}class JS{constructor(e,t,n,s=!1,i=!1,o=!1,a=null,c=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;const h=s?e[1]:e[2],d=Math.ceil(h/2),m=s?"i * 2, rc.y":"rc.y, i * 2",f=i?"rc.z, i * 2":"i * 2, rc.z",b=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],w=i?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let L="",x="";a&&(c?L=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${a}
}`:L=`vec4 activation(vec4 x) {
${a}
}`,x="result = activation(result);");const v=o?"result += getBiasAtOutCoords();":"";o&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights");let N="rc.x",O="rc.x";e[0]<t[0]?N=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(O=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${L}
const float sharedDimension = ${d}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${d}; i++) {
int batchA = ${N};
int batchB = ${O};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${b[0]} * ${w[0]});
result += (${b[1]} * ${w[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${v}
${x}
setOutput(result);
}
`}}class X6{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 J6{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 Z6{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=Mn("rc",t),s=Rt(t),i=eX(t,e,n),o=tX(t,e[e.length-1],e[e.length-2],n),a=nX(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${i}) {
setOutput(vec4(0));
} else {
${o}
setOutput(vec4(${a}));
}
}
`}}}function Q6(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 eX(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 tX(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 nX(e,t){const n=e.length,s=Q6(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 sX{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((h,d)=>h[0]+e[d]+h[1]);const s=e.length,i=Rt(s),o=t.map(h=>h[0]).join(","),a=t.map((h,d)=>h[0]+e[d]).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 iX{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((L,x)=>L[0]+e[x]+L[1]);const s=e.length,i=Rt(s),o=t.map(L=>L[0]).join(","),a=t.map((L,x)=>L[0]+e[x]).join(","),c=Mn("rc",s),h=Mn("source",s),d=`${c[s-1]} < ${this.outputShape[s-1]}`,m=s===1?"source":`vec2(${h.slice(-2).join()})`,f=[`${i} rc = outputLoc;`,`${c[s-1]} += 1;
if(${d}) {
`,s===1?"":`}
rc = outputLoc;
${c[s-2]} += 1;
if(${c[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${c[s-1]} += 1;
if(${d}) {`],b=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let w="";for(let L=0,x=s===1?2:4;L<x;L++)w+=`
${f[L]}
if (${b}) {
result[${L}] = float(${n});
} else {
${i} source = rc - start;
result[${L}] = getChannel(getX(${h.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 Tu{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,h=e.dilationHeight,d=e.dilationWidth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,b=e.padInfo.top,w=e.padInfo.left;this.outputShape=e.outShape;const L=t==="avg",x=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,v=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let N="0.0";if(L||(N="-1.0 / 1e-20"),n){const $=">=";this.userCode=`
const ivec2 strides = ivec2(${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 += ${h}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${d}) {
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 ${$} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?i?x:v:`wR * ${f} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const O="max";let E=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(E="avgValue / count");const k=Math.floor(o/4)*4,F=o%4,U=`
if (${L}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${O}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${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 += ${h}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${d};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
getValue(batch, xR, xC + 2 * ${d}, d),
getValue(batch, xR, xC + 3 * ${d}, d)
);
${U}
}
int xC = xCCorner + ${k};
if (${F===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${U}
} else if (${F===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
initializationValue,
initializationValue
);
${U}
} else if (${F===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
getValue(batch, xR, xC + 2 * ${d}, d),
initializationValue
);
${U}
}
}
setOutput(${E});
}
`}}class ZS{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,h=e.strideWidth,d=e.dilationDepth,m=e.dilationHeight,f=e.dilationWidth,b=e.effectiveFilterDepth,w=e.effectiveFilterHeight,L=e.effectiveFilterWidth,x=e.padInfo.front,v=e.padInfo.top,N=e.padInfo.left;this.outputShape=e.outShape;const O=t==="avg";let E="0.0";if(O||(E="-1.0 / 1e-20"),n){const j=">=";this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${h});
const ivec3 pads = ivec3(${x}, ${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 += ${d}) {
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 < ${L};
wC += ${f}) {
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 ${j} 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} * ${L} +
wR * ${L} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const k="max";let F=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(F="avgValue / count");const U=Math.floor(o/4)*4,$=o%4,Y=`
if (${O}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${h});
const ivec3 pads = ivec3(${x}, ${v}, ${N});
const float initializationValue = ${E};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${E});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${b};
wD += ${d}) {
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 < ${U}; wC += 4) {
int xC = xCCorner + wC * ${f};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${f}, ch),
getValue(batch, xD, xR, xC + 2 * ${f}, ch),
getValue(batch, xD, xR, xC + 3 * ${f}, ch)
);
${Y}
}
int xC = xCCorner + ${U};
if (${$===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${Y}
} else if (${$===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${f}, ch),
initializationValue,
initializationValue
);
${Y}
} else if (${$===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${f}, ch),
getValue(batch, xD, xR, xC + 2 * ${f}, ch),
initializationValue
);
${Y}
}
}
setOutput(${F});
}
}
`}}class TC{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 h=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?h="sumValue":t==="prod"?h="prodValue":t==="all"?h="allValue":t==="any"&&(h="anyValue");const d=Math.floor(n/4)*4,m=n%4;let f=`
if (${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",f=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,b="bvec4"):t==="any"&&(a="0.0",f=`
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 < ${d}; i += 4) {
int inIdx = inOffset + i;
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${f}
}
int inIdx = inOffset + ${d};
if (${m===1}) {
${b} values = ${b}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${f}
} else if (${m===2}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${f}
} else if (${m===3}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${f}
}
setOutput(${h});
}
`}}class AC{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=`
${rX(t)}
${XS(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}}function rX(e){const t=ea(["r","c","d"],e);return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t}
return ivec3(r, c, d);
}
`}class oX{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,s,i]=t.shape,[,o,a]=e.shape,c=[n&&o>1?s-1:s,n&&a>1?i-1:i],h=[n&&o>1?o-1:o,n&&a>1?a-1:a],d=c[0]/h[0],m=c[1]/h[1],f=1/d,b=1/m,w=Math.ceil(f)*2+2,L=Math.ceil(b)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${d});
const float widthScale = float(${m});
const float invHeightScale = float(${f});
const float invWidthScale = float(${b});
const int winHeight = int(${w});
const int winWidth = int(${L});
// 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 aX{constructor(e,t,n,s){this.variableNames=["A"],this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const h=[s&&t>1?o-1:o,s&&n>1?a-1:a],d=[s&&t>1?t-1:t,s&&n>1?n-1:n];this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${h[0]/d[0]},
${h[1]/d[1]});
const vec2 inputShapeRC = vec2(${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 cX{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 h=[s&&t>1?o-1:o,s&&n>1?a-1:a],d=[s&&t>1?t-1:t,s&&n>1?n-1:n];this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${h[0]/d[0]},
${h[1]/d[1]},
${h[1]/d[1]});
const vec3 inputShapeRC = vec3(${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 lX{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],h=[n&&o>1?o-1:o,n&&a>1?a-1:a],d=c[0]/h[0],m=c[1]/h[1],f=1/d,b=1/m,w=Math.ceil(f)*2+2,L=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(${d});
const float widthScale = float(${m});
const float invHeightScale = float(${f});
const float invWidthScale = float(${b});
const int winHeight = int(${w});
const int winWidth = int(${L});
// 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(${h[0]}));
float sourceFracCol =
float(${c[1]}) *
(float(dyC) / float(${h[1]}));
int sourceNearestRow = int(min(
float(int(${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 hX{constructor(e,t,n,s){this.variableNames=["A"],this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const h=[s&&t>1?o-1:o,s&&n>1?a-1:a],d=[s&&t>1?t-1:t,s&&n>1?n-1:n],m=s?"0.5":"0.0";this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${h[0]/d[0]},
${h[1]/d[1]});
const vec2 inputShapeRC = vec2(${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 + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}}class uX{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=Rt(n);this.userCode=`
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}}class dX{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-${n} tensor is not yet supported`);this.outputShape=e;const s=Mn("rc",n),i=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,o=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Rt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${h(s.slice())};
}
if(${o}) {
result.b = ${d(s.slice())};
if(${i}) {
result.a = ${m(s.slice())};
}
}
setOutput(result);
}
`;function c(w){return f(w)}function h(w){return w[n-1]="("+w[n-1]+" + 1)",f(w)}function d(w){return w[n-2]="("+w[n-2]+" + 1)",f(w)}function m(w){return w[n-1]="("+w[n-1]+" + 1)",w[n-2]="("+w[n-2]+" + 1)",f(w)}function f(w){const L=e.map((N,O)=>b(O,w)),x=L.join(","),v=L.slice(-2).join(",");return`getChannel(getX(${x}), vec2(${v}))`}function b(w,L){return t.indexOf(w)!==-1&&e[w]!==1?`${e[w]} - ${L[w]} - 1`:`${L[w]}`}}}class vC{constructor(e,t,n,s,i,o,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=o;const c=Rt(i.length),h=Rt(o.length);let d="";n===1?d="i":n===2&&(d="i, j");const m=`getIndices(${d})`;let f="";s===1?f="i":s===2&&(f="i, coords[1]");const b=`getUpdates(${f})`,w=t>1?"strides[j]":"strides";this.userCode=`
${c} strides = ${c}(${i});
void main() {
${h} 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 pX{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",h="sumValue",d=Math.floor(n/4)*4,m=n%4,f=`
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 < ${d}; 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
);
${f}
}
int inIdx = inOffset + ${d};
if (${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
);
${f}
} else if (${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
);
${f}
} else if (${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
);
${f}
}
setOutput(${h});
}
`}}class mX{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,i;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)i="resRC",s="resRC";else{const a=["resRC.x","resRC.y","resRC.z","resRC.w"],c=[],h=[];for(let d=0;d<t.length;d++)h.push(`${a[d]}`),d<e&&c.push(`${a[d]}`);s=c.join(),i=h.join()}const o=Rt(n);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${i}));
} else {
setOutput(getB(${i}));
}
}
`}}class fX{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Rt(this.rank),n=`uniform int start[${this.rank}];`,s=gX(this.rank);let i;const o=e.map((a,c)=>`sourceLoc.${QS[c]} = start[${c}] + coords.${QS[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 QS=["x","y","z","w","u","v"];function gX(e){if(e===1)return"sourceLoc";if(e<=6)return QS.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class yX{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Rt(this.rank),n=Mn("coords",this.rank),s=Mn("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${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};
}
}
`,h=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((d,m)=>`start[${m}]`).join()});`:e.map((d,m)=>`${s[m]} = ${n[m]} + start[${m}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${h}
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 (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class bX{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,i=Rt(n.length),o=Rt(n.length);let a="";if(s===1)a="coords * strides + begin";else{let c=0;a=n.map((h,d)=>(c++,n.length===1?`coords * strides[${d}] + begin[${d}]`:`coords[${c-1}] * strides[${d}] + begin[${d}]`)).join(",")}this.userCode=`
${i} begin = ${i}(${e});
${i} strides = ${i}(${t});
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}}class wX{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=CC(t,n),i=RC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=NC(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===Cn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Cn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Cn.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=CC(n,s),o=RC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=NC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=oe().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 h=this.usedTextures[o],d=h.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");h.splice(d,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 LX(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 NC(e,t,n,s,i){const o=SX(t,s);let a;if(i){const[h,d]=wc(e[0],e[1]);a=h*d}else{const[h,d]=Lu(e[0],e[1]);a=h*d}const c=LX(n,o);return a*c}function SX(e,t){switch(e){case Cn.PACKED_2X2_FLOAT32:return SC(t);case Cn.PACKED_2X2_FLOAT16:return IC(t);case Cn.UNPACKED_FLOAT32:return bC(t);case Cn.UNPACKED_FLOAT16:return wC(t);case Cn.PACKED_4X1_UNSIGNED_BYTE:return LC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function IX(e){return oe().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Cn.PACKED_2X2_FLOAT32:Cn.UNPACKED_FLOAT32:e?Cn.PACKED_2X2_FLOAT16:Cn.UNPACKED_FLOAT16}function CC(e,t){if(e===Ns.UPLOAD)return Cn.PACKED_2X2_FLOAT32;if(e===Ns.RENDER||e==null)return IX(t);if(e===Ns.DOWNLOAD||e===Ns.PIXELS)return Cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function RC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class xX{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=Rt(this.rank),i=TX(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function TX(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 st{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;",AX="return x;",OC="return abs(x);",EC=dr+`
return (x < 0.0) ? 0.0 : x;
`,DC=dr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,kC="return (x >= 0.0) ? x : (exp(x) - 1.0);",vX=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${Hp};
float scale = ${qp};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function NX(e=0){return dr+`
return x > 0.0 ? 1.0 : float(${e});
`}const FC="return -x;",_C="return ceil(x);",WC="return floor(x);",CX=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,RX="return float(isnan(x));",OX="return float(isinf(x));",EX="return float(!isnan(x) && !isinf(x));",DX=`
// 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;
}
}
`,$C="return exp(x);",UC="return exp(x) - 1.0;",kX=`if (x < 0.0) return NAN;
return log(x);`,FX="return log(1.0 + x);",_X="return sqrt(x);",WX="return inversesqrt(x);",$X="return 1.0 / (1.0 + exp(-1.0 * x));",UX=`
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;
`,BX=dr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,MX=dr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,PX=dr+`
return atan(x);
`,zX=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,VX=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,GX=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,YX=dr+"return log(x + sqrt(x * x + 1.0));",HX=dr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,qX=dr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,jX=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${Cw};
float a1 = ${Rw};
float a2 = ${Ow};
float a3 = ${Ew};
float a4 = ${Dw};
float a5 = ${kw};
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));
`,KX="return 1.0 / x;",XX="return float(!(x >= 1.0));",Gm="return x;";const JX="return x;",ZX=`
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;
`,BC=`
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;
`,MC=`
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;
`,PC=`
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 Au{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}}class QX{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=Mn("rc",t),s=Rt(t),i=z5(t,n),o=n.slice(-2),a=t<=1?"rc":`vec2(${o.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${i});
setOutput(getChannel(packedInput, ${a}));
}
`}}const{segment_util:zC}=Ww,e7=$w,t7=Uw,n7=Bw,s7=_p,i7=1e-7,r7=1e-4,Ym={};function o7(e){return e in Ym||(Ym[e]={}),Ym[e]}function Hm(e,t=!1){if(e==="linear")return t?JX:AX;if(e==="relu")return t?BC:EC;if(e==="elu")return t?PC:kC;if(e==="relu6")return t?MC:DC;if(e==="prelu")return t?uC:hC;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const a7=128,c7=600;function l7(){return oe().global.screen==null?1024:oe().global.screen.height*oe().global.screen.width*window.devicePixelRatio*c7/1024/1024}const VC=1e3;class h7 extends y{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,!oe().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Fi(oe().getNumber("WEBGL_VERSION"));this.binaryCache=o7(oe().getNumber("WEBGL_VERSION")),this.gpgpu=new B6(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 wX(this.gpgpu),this.numMBBeforeWarning=l7(),this.texData=new p(this,Ji())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((oe().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||oe().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:Ns.UPLOAD,refCount:1,complexParentRefCount:0}),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(oe().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:Ns.UPLOAD,refCount:1,complexParentRefCount:0})}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,complexTensorInfos:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let f;c?f=new Au(a,Gm):f=new st(a,Gm);const b=this.runWebGLProgram(f,[{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 h=this.activeTimers!=null;let d;h&&(d=Kn());let m;if(s==="complex64"){const f=this.readSync(i.real.dataId),b=this.readSync(i.imag.dataId);m=sr(f,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Kn()-d),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(L=>w.push(L))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensorInfos:a,isPacked:c}=t;if(i!=null){let w;c?w=new Au(s,Gm):w=new st(s,Gm);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),x=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),x}if(n!=null)return this.convertAndCacheOnCPU(e);if(!oe().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&oe().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let h=null,d;if(o!=="complex64"&&oe().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...Su(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),L=w[0],x=w[1];m=sr(L,x)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=P(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const f=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(f)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),f}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!e5(n))throw oe().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=P(t);if(oe().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const f=this.decode(e),b=this.texData.get(f.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...Su(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(f),w}const o=oe().getBool("WEBGL_PACK")&&s===!0,a=o?qS(t):t,c=o?new g6(a):new f6(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=te(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=te(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=C(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${h.name}: ${h.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 oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Kn(),endMs:null}}endTimer(e){return oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Kn(),e)}async getQueryTime(e){if(oe().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;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);const{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),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,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return oe().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ji().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=a7){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&&P(s.shape)<t)}getGPGPUContext(){return this.gpgpu}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=W5(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(P(n)===0)return sn([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Cb(e.shape,t,n);if(s||!i){const o=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yX(n):new fX(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=Rb(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=ip(t,n,s);if(o.some(c=>c===0))return sn([],o);const a=new bX(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dX(e.shape,t):new uX(e.shape,t);return this.compileAndRun(n,[e])}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(oe().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,FC,e.dtype);const n=new st(e.shape,FC);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=Math.max(e.shape[0],t.shape[0]);if((i===1||o===1)&&a>VC){n&&(e=Ye(e,[0,2,1])),s&&(t=Ye(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),f=o===1?2:1,b=o===1?t.as3D(c,1,a):t,w=X(m,b);return w.sum(f,!0)}const h=$n(e.dtype,t.dtype),d=new JS(e.shape,t.shape,[c,i,o],n,s);return this.compileAndRun(d,[e,t],h)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],h=s?t.shape[1]:t.shape[2],d=Math.max(e.shape[0],t.shape[0]),m=$n(e.dtype,t.dtype),f=i!=null,b=a!=null,w=o?Hm(o,!0):null,L=new JS(e.shape,t.shape,[d,c,h],n,s,f,w,b),x=[e,t];return i&&x.push(i),a&&x.push(a),this.compileAndRun(L,x,m)}localResponseNormalization4D(e,t,n,s,i){const o=oe().getBool("WEBGL_PACK_NORMALIZATION")?new q6(e.shape,t,n,s,i):new Y6(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new H6(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=>rh(a)),o=wt(e.shape,e.dtype,i);return t7(o,t)}const n=new xX(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iX(e.shape,t,n):new 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oX(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new hX(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new lX(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:Vo(e),o=i.shape[0],a=i.shape[1],c=new X6(o,a,n),h=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",h)}oneHot(e,t,n,s){const i=new J6(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new m6(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new h6(e.shape,t.shape,s,i,o);return this.compileAndRun(a,[e,t,n],"float32")}depthToSpace(e,t,n){A(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const s=e.shape[0],i=n==="NHWC"?e.shape[1]:e.shape[2],o=n==="NHWC"?e.shape[2]:e.shape[3],a=n==="NHWC"?e.shape[3]:e.shape[1],c=i*t,h=o*t,d=a/(t*t),m=n==="NHWC"?[s,c,h,d]:[s,d,c,h],f=new p6(m,t,n);return this.compileAndRun(f,[e])}split(e,t,n){return e7(e,t,n)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=Pa(t,e,n),h=[c/o,o],d=e.reshape([i,s]),m=t.reshape([i,o]);if(c===0)return iv(sn([]),n);const f=Ce(0),b=new vC(i,s,d.rank,m.rank,a,h),w=this.compileAndRun(b,[m,d,f]);return w.reshape(n)}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,strides:a,outputSize:c}=Pa(t,e,n),h=!1,d=new vC(o,i,e.rank,t.rank,a,[c,1],h),m=this.compileAndRun(d,[t,e,s]);return m.reshape(n)}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=np(e,t),h=t.reshape([o,s]),d=e.reshape([e.size/a,a]),m=new I6(s,c,[o,a]),f=this.compileAndRun(m,[d,h]);return f.reshape(i)}fill(e,t,n){if(n=n||Aa(t),n==="string"){const s=ws(n,P(e));return s.fill(t),Ji().makeTensor(s,e,n,this)}else{const s=new w6(e,t),i=s.getCustomSetupFunc(t);return this.compileAndRun(s,[],n,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,n){return _w(e,t,n)}makeTensorInfo(e,t,n){const s=this.write(n,e,t);return 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 Ji().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new QX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new Z6(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[Lc(e.shape),...Sc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[Lc(t),...Sc(t)],o=new AC(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=qS(s);let a;n?a=new d6(o):a=new u6(o);const c=!0,h=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:h.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===wu.DENSE){const L=Su(e.outputShape);a.texShape=L.map(x=>x*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),P(o.shape)===0)return a.values=bt(o.dtype,0),o;const c=[],h=t.map(L=>{if(L.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(L.dataId);if(x.texture==null){if(!e.packedInputs&&P(L.shape)<=oe().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=L.shape)}else if(!!x.isPacked!==!!e.packedInputs)L=x.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),x=this.texData.get(L.dataId);else if(x.isPacked&&!Bm(x.shape,L.shape)){const v=L,N=L.shape;L.shape=x.shape,L=this.packedReshape(L,N),c.push(L),x=this.texData.get(L.dataId),v.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:x,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=V6(e,h,d),f=this.getAndSaveBinary(m,()=>P6(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),z6(this.gpgpu,f,h,d,s),c.forEach(L=>this.disposeIntermediateTensorInfo(L)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!oe().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const L=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),L}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return Ji().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(!oe().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=Q(()=>{if(!oe().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=oe().getBool("DEBUG");oe().set("DEBUG",!1);const t=this.abs(Ce(1e-8)).dataSync()[0];if(oe().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?i7:r7}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 h=this.activeTimers!=null;let d;h&&(d=Kn());let m=t.texShape;if(m==null&&(m=b5(n,c),t.texShape=m),i!=null){const f=qS(n);let b,w=m[1],L=m[0];const x=i instanceof Uint8Array;c?([w,L]=wc(m[0],m[1]),b=new b6(f,[L,w],x)):b=new y6(f,[L,w],x);const v=this.makeTensorInfo([L,w],s);x?this.texData.get(v.dataId).usage=Ns.PIXELS:this.texData.get(v.dataId).usage=Ns.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(v.dataId),w,L,i);const N=!0,O=this.runWebGLProgram(b,[v],s,null,N),E=this.texData.get(O.dataId);t.texture=E.texture,t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,this.disposeIntermediateTensorInfo(v),this.texData.delete(O.dataId),t.values=null,h&&(this.uploadWaitMs+=Kn()-d)}else{const f=this.acquireTexture(m,a,s,c);t.texture=f}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=u7(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]*py(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(oe().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function u7(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 ${t}`)}const d7="2.7.0";function p7(){oe().set("WEBGL_FORCE_F16_TEXTURES",!0)}db()&&kb("webgl",()=>new h7,2);const cne={forceHalfFloat:p7};function pr(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const m7={kernelName:kl,backendName:"webgl",kernelFunc:pr};function vc(e){const{inputs:t,backend:n}=e,{real:s,imag:i}=t,o=n.makeTensorInfo(s.shape,"complex64"),a=n.texData.get(o.dataId),c=pr({inputs:{x:s},backend:n}),h=n.texData.get(c.dataId);h.complexParentRefCount++;const d=pr({inputs:{x:i},backend:n}),m=n.texData.get(d.dataId);return m.complexParentRefCount++,a.complexTensorInfos={real:c,imag:d},o}const f7={kernelName:fd,backendName:"webgl",kernelFunc:vc};const GC="if (isnan(x)) return x;",g7=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,y7=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function qm(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new st(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function Nc({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:i,dtype:o}){return({inputs:a,backend:c})=>{const{a:h,b:d}=a,m=c;if(s&&h.dtype==="complex64"){const L=m.texData.get(h.dataId),x=m.texData.get(d.dataId),[v,N]=[[L.complexTensorInfos.real,x.complexTensorInfos.real],[L.complexTensorInfos.imag,x.complexTensorInfos.imag]].map(E=>{const[k,F]=E,U={dataId:k.dataId,dtype:k.dtype,shape:h.shape},$={dataId:F.dataId,dtype:F.dtype,shape:d.shape},Y=new Wn(e,h.shape,d.shape);return m.runWebGLProgram(Y,[U,$],$n(k.dtype,F.dtype))}),O=vc({inputs:{real:v,imag:N},backend:m});return m.disposeIntermediateTensorInfo(v),m.disposeIntermediateTensorInfo(N),O}const f=o||$n(h.dtype,d.dtype);if(m.shouldExecuteOnCPU([h,d])&&i!=null){const L=m.texData.get(h.dataId),x=m.texData.get(d.dataId),[v,N]=i(h.shape,d.shape,L.values,x.values,f),O=m.makeTensorInfo(N,f),E=m.texData.get(O.dataId);return E.values=v,O}const b=oe().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null;let w;return b?w=new ur(t,h.shape,d.shape,n):w=new Wn(e,h.shape,d.shape),m.runWebGLProgram(w,[h,d],f)}}const YC="return a + b;",b7=Nc({opSnippet:YC,packedOpSnippet:YC,supportsComplex:!0,cpuKernelImpl:N5}),w7={kernelName:Co,backendName:"webgl",kernelFunc:b7};const L7=g7+`
return atan(a, b);
`,S7=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+y7+`
return result;
`,I7=Nc({opSnippet:L7,packedOpSnippet:S7}),x7={kernelName:dd,backendName:"webgl",kernelFunc:I7};function T7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Iu(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;A(cn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Un(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ae(m.inShape,m.outShape))return pr({inputs:{x:i},backend:n});const f=new Tu(m,"avg",!1);return n.runWebGLProgram(f,[i],"float32")}const A7={kernelName:Il,backendName:"webgl",kernelFunc:T7};function v7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;Iu([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Un(a.shape,c,h,1,d),f=new v8(m);return n.runWebGLProgram(f,[i],a.dtype)}const N7={kernelName:pd,backendName:"webgl",kernelFunc:v7};class C7{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 R7{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 O7=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;A(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(c==null||i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:h}=n;h==null&&(h=.001);const d=[s,i,o];let m=null;a!=null&&(m=a.shape,d.push(a));let f=null;c!=null&&(f=c.shape,d.push(c));const b=oe().getBool("WEBGL_PACK_NORMALIZATION")?new R7(s.shape,i.shape,o.shape,m,f,h):new C7(s.shape,i.shape,o.shape,m,f,h),w=t.runWebGLProgram(b,d,d[0].dtype);return w},E7={kernelName:Dl,backendName:"webgl",kernelFunc:O7};const D7="return float(a != b);",HC=Nc({opSnippet:D7,dtype:"bool"}),k7={kernelName:zl,backendName:"webgl",kernelFunc:HC};function eI(e){const{inputs:t,backend:n}=e,{input:s}=t,i=n.texData.get(s.dataId);return pr({inputs:{x:i.complexTensorInfos.real},backend:n})}const F7={kernelName:Fd,backendName:"webgl",kernelFunc:eI};const _7="return float(int(x));";function W7(e,t){const n=new st(e.shape,_7),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function tI(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return pr({inputs:{x:i},backend:n});const a=dt(i.shape),c=tI({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=vc({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=eI({inputs:{input:i},backend:n}),c=tI({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!Ta(i.dtype,o)){const a=pr({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32")return W7(i,n);if(o==="bool"){const a=n.makeTensorInfo([],"bool",bt("bool",1)),c={a:i,b:a},h=HC({inputs:c,backend:n});return n.disposeIntermediateTensorInfo(a),h}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const $7={kernelName:Na,backendName:"webgl",kernelFunc:tI};class U7{constructor(e){this.outputShape=[],this.outputShape=Zi(e,1),this.variableNames=e.map((o,a)=>`T${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;
${n.join(`
`)}
}
`}}class B7{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Zi(e,t);const n=this.outputShape,s=n.length,i=Rt(s),o=Mn("coords",s),a=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((L,x)=>`T${x}`);const c=new Array(e.length-1);c[0]=e[0][t];for(let L=1;L<c.length;L++)c[L]=c[L-1]+e[L][t];const h=a[t],d=a.slice(-2),m=a.join();let f=`if (${h} < ${c[0]}) {
return getChannel(
getT0(${m}), vec2(${d.join()}));
}`;for(let L=1;L<c.length;L++){const x=c[L-1];f+=`
if (${h} < ${c[L]} && ${h} >= ${c[L-1]}) {
return getChannel(
getT${L}(${jm(a,h,x)}),
vec2(${jm(d,h,x)}));
}`}const b=c.length,w=c[c.length-1];f+=`
return getChannel(
getT${b}(${jm(a,h,w)}),
vec2(${jm(d,h,w)}));`,this.userCode=`
float getValue(${a.map(L=>"int "+L)}) {
${f}
}
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 jm(e,t,n){const s=e.indexOf(t),i=e.map((o,a)=>a===s?`${o} - ${n}`:o);return i.join()}function qC(e){const{inputs:t,backend:n}=e,{input:s}=t,i=n.texData.get(s.dataId);return pr({inputs:{x:i.complexTensorInfos.imag},backend:n})}const M7={kernelName:vd,backendName:"webgl",kernelFunc:qC};function P7(e,t,n){const s=[Lc(e.shape),...Sc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[Lc(t),...Sc(t)],a=new AC(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function mr(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=P(i.shape),h=Gt(o,c),d=P(h);A(c===d,()=>`The new shape (${h}) has ${d} elements and the old shape (${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&&!Bm(i.shape,h)&&!(m.texture!==null&&Bm(m.shape,h))?P7(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const z7={kernelName:Yl,backendName:"webgl",kernelFunc:mr};function Cc(e,t,n){const s=e[0].dtype;if(s==="complex64"){const d=e.map(L=>eI({inputs:{input:L},backend:n})),m=e.map(L=>qC({inputs:{input:L},backend:n})),f=Cc(d,t,n),b=Cc(m,t,n),w=vc({inputs:{real:f,imag:b},backend:n});return d.forEach(L=>n.disposeIntermediateTensorInfo(L)),m.forEach(L=>n.disposeIntermediateTensorInfo(L)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),w}if(e.length>oe().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const d=Math.floor(e.length/2),m=Cc(e.slice(0,d),t,n),f=Cc(e.slice(d),t,n),b=Cc([m,f],t,n);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),b}if(oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){const d=new B7(e.map(m=>m.shape),t);return n.runWebGLProgram(d,e,s)}const i=Zi(e.map(d=>d.shape),t),o=e.map(d=>mr({inputs:{x:d},attrs:{shape:[-1,P(d.shape.slice(t))]},backend:n})),a=new U7(o.map(d=>d.shape)),c=n.runWebGLProgram(a,o,s);o.forEach(d=>n.disposeIntermediateTensorInfo(d));const h=mr({inputs:{x:c},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(c),h}function V7(e){const{inputs:t,backend:n,attrs:s}=e,{axis:i}=s,o=qe(i,t[0].shape)[0],a=Zi(t.map(d=>d.shape),o);if(P(a)===0)return n.makeTensorInfo(a,t[0].dtype,[]);const c=t.filter(d=>P(d.shape)>0);if(c.length===1)return c[0];const h=c.map(d=>d.shape);return dp(h,o),Cc(c,o,n)}const G7={kernelName:Al,backendName:"webgl",kernelFunc:V7};const Y7=GC+`
return cos(x);
`,H7=qm(Y7),q7={kernelName:Ca,backendName:"webgl",kernelFunc:H7};const j7=`
if (a == b) {
return 1.0;
};
return a / b;`,K7=`
// 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;
`,X7=Nc({opSnippet:j7,packedOpSnippet:K7,checkOutOfBounds:!0}),J7={kernelName:Ra,backendName:"webgl",kernelFunc:X7};class jC{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";let a;if(e==="real")a="return real * expR - imag * expI;";else if(e==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${i};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${a}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${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]));
}
`}}function KC(e,t,n){const s=n.texData.get(e.dataId),i=P(e.shape),o=e.shape[e.shape.length-1],a=i/o,c=mr({inputs:{x:e},backend:n,attrs:{shape:[a,o]}}),h=c.shape,d=new jC("real",h,t),m=new jC("imag",h,t),f=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:h},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:h}],b=n.runWebGLProgram(d,f,"float32"),w=n.runWebGLProgram(m,f,"float32"),L=vc({inputs:{real:b,imag:w},backend:n});n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(w);const x=mr({inputs:{x:L},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(x),x}function Z7(e){const{inputs:t,backend:n}=e,{input:s}=t;return KC(s,!1,n)}const Q7={kernelName:xd,backendName:"webgl",kernelFunc:Z7};class eJ{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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}}const tJ={kernelName:Td,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,i=new eJ(n.shape),o=s.runWebGLProgram(i,[n],n.dtype);return o}};class nJ{constructor(e){this.variableNames=["A"];const t=Pn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}}class sJ{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=Pn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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 iJ={kernelName:Bd,backendName:"webgl",kernelFunc:rJ};let Rc;function rJ(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,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],f=[d,h,o];(c||a)&&(Rc==null&&(Rc=document.createElement("canvas").getContext("2d")),Rc.canvas.width=h,Rc.canvas.height=d,Rc.drawImage(i,0,0,h,d),i=Rc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=Ns.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=oe().getBool("WEBGL_PACK")?new sJ(f):new nJ(f),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function oJ(e){const{inputs:t,backend:n}=e,{input:s}=t;return KC(s,!0,n)}const aJ={kernelName:Ad,backendName:"webgl",kernelFunc:oJ};class XC{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:i,outSize:o}=e;this.outputShape=[s,o];const a=Math.floor(n/4)*4,c=n%4;let h="sumValue += dot(values, ones);";if(t!=null){const m=1/t;h=`sumValue += dot(values * ${Le(m)?m.toPrecision(2):m}, ones);`}let d="";i%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${i}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${a}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${a};
if (${c===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${h}
} else if (${c===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${h}
} else if (${c===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${h}
}
setOutput(sumValue);
}
`}}function cJ(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=Sh(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function JC(e,t,n,s){const i=cJ(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a];let m,f;n==="mean"?m=a===0?new XC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},c):new XC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d}):m=new TC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),f=o,o=s.runWebGLProgram(m,[o],t),f.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(f)}return o}function lJ(e,t,n,s){const i=P(t),o=P(e.shape),a=o/i,c=mr({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=JC(c,e.dtype,"max",s),d=mr({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class hJ{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=Rt(this.rank),i=uJ(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function uJ(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 dJ{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let d=0;d<n.length;d++)n[d]=e[t[d]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const s=Rt(this.rank),i=iC("rc",this.rank),o=new Array(this.rank);for(let d=0;d<t.length;d++)o[t[d]]=i[d];const a=`vec2(${o.slice(-2).join()})`,c=`++${i[this.rank-1]} < ${n[this.rank-1]}`,h=`getChannel(getA(${o.join()}), ${a})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${h};
if(${c}) {
result[1] = ${h};
}
--${i[this.rank-1]};
if(++${i[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${h};
if(${c}) {
result[3] = ${h};
}
}
setOutput(result);
}
`}}function nI(e,t,n){const s=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dJ(e.shape,t):new hJ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const pJ={kernelName:Bl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=qe(i,s.shape);let d=h;const m=Jn(d,c),f=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(f){if(b){const O=a.texData.get(w.dataId),E=O.values,k=new Array(c);for(let $=0;$<k.length;$++)k[$]=s.shape[m[$]];const F=KS(E,s.shape,s.dtype,m,k);w=a.makeTensorInfo(k,s.dtype);const U=a.texData.get(w.dataId);U.values=F}else w=nI(s,m,a);d=cs(d.length,c)}Xn("max",d,c);const[L,x]=An(w.shape,d);let v=L;o&&(v=vn(L,h));let N;if(b){const O=a.texData.get(w.dataId),E=O.values,k=k5(E,P(x),v,s.dtype);N=a.makeTensorInfo(v,s.dtype);const F=a.texData.get(N.dataId);F.values=k}else N=lJ(w,x,v,a);return f&&a.disposeIntermediateTensorInfo(w),N}};function mJ(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Iu(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;A(cn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Un(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ae(m.inShape,m.outShape))return pr({inputs:{x:i},backend:n});const f=new Tu(m,"max",!1);return n.runWebGLProgram(f,[i],i.dtype)}const fJ={kernelName:Ml,backendName:"webgl",kernelFunc:mJ};function gJ(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;Iu([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:f}=s,b=Un(c.shape,h,d,1,m,f),w=!0,L=new Tu(b,"max",w),x=n.runWebGLProgram(L,[c],c.dtype),v=new j6(b),N=n.runWebGLProgram(v,[i,x],c.dtype);return n.disposeIntermediateTensorInfo(x),N}const yJ={kernelName:Cd,backendName:"webgl",kernelFunc:gJ};function bJ(e,t,n,s){let i=new Tu(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new Tu(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const wJ={kernelName:Rd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;A(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];A(cn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Un(s.shape,i,o,d,a),[f,b]=bJ(s,c,m,h);return[f,b]}};function LJ(e,t,n,s){const i=P(t),o=P(e.shape),a=o/i,c=mr({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=JC(c,"float32","mean",s),d=mr({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}const SJ={kernelName:Uy,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{keepDims:i,axis:o}=t,a=n,c=s.shape.length,h=qe(o,s.shape);let d=h;const m=Jn(d,c),f=m!=null,b=a.shouldExecuteOnCPU([s]),w=[];let L=s;if(f){if(b){const E=a.texData.get(L.dataId),k=E.values,F=new Array(c);for(let Y=0;Y<F.length;Y++)F[Y]=s.shape[m[Y]];const U=KS(k,s.shape,s.dtype,m,F);L=a.makeTensorInfo(F,s.dtype);const $=a.texData.get(L.dataId);$.values=U}else L=nI(s,m,a);w.push(L),d=cs(d.length,c)}Xn("sum",d,c);const[x,v]=An(L.shape,d);let N=x;i&&(N=vn(x,h));const O=LJ(L,v,N,a);for(const E of w)a.disposeIntermediateTensorInfo(E);return O}};class IJ{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((d,m)=>d[0]+e[m]+d[1]);const s=e.length,i=Rt(s),o=t.map(d=>d[0]).join(","),a=t.map((d,m)=>d[0]+e[m]).join(","),c=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),h=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${o};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${h};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${h};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${i} start = ${i}(${o});
${i} end = ${i}(${a});
void main() {
${i} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${h};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${h};
}
}
${i} coords = outC - start;
setOutput(getX(${c}));
}
`}}class xJ{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((w,L)=>w[0]+e[L]+w[1]);const s=e.length,i=Rt(s),o=t.map(w=>w[0]).join(","),a=t.map((w,L)=>w[0]+e[L]).join(","),c=Mn("rc",s),h=Mn("source",s),d=`${c[s-1]} < ${this.outputShape[s-1]}`,m=s===1?"source":`vec2(${h.slice(-2).join()})`,f=n==="reflect"?0:1;let b="";if(s===1){const w=`
${i} source = rc;
if (source < start) {
source = start * 2 - source - ${f};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${f};
}
source -= start;
`;b=`
${i} rc = outputLoc;
${w}
result[0] = getChannel(getX(${h.join()}), ${m});
${c[s-1]} += 1;
if(${d}) {
${w}
result[1] = getChannel(getX(${h.join()}), ${m});
}
`}else{const w=`
${i} source = rc;
${i} lt = ${i}(lessThan(source, start));
${i} gte = ${i}(greaterThanEqual(source, end));
${i} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${f}) +
gte * ((end - 1) * 2 - source + ${f});
source -= start;
`;b=`
${i} rc = outputLoc;
${w}
result[0] = getChannel(getX(${h.join()}), ${m});
${c[s-1]} += 1;
if(${d}) {
${w}
result[1] = getChannel(getX(${h.join()}), ${m});
}
rc = outputLoc;
${c[s-2]} += 1;
if(${c[s-2]} < ${this.outputShape[s-2]}) {
${w}
result[2] = getChannel(getX(${h.join()}), ${m});
${c[s-1]} += 1;
if(${d}) {
${w}
result[3] = getChannel(getX(${h.join()}), ${m});
}
}
`}this.userCode=`
const ${i} start = ${i}(${o});
const ${i} end = ${i}(${a});
void main() {
${i} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${b}
setOutput(result);
}
`}}const TJ=({inputs:e,backend:t,attrs:n})=>{const{x:s}=e,{paddings:i,mode:o}=n,a=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xJ(s.shape,i,o):new IJ(s.shape,i,o),c=t.runWebGLProgram(a,[s],s.dtype);return c},AJ={kernelName:Pl,backendName:"webgl",kernelFunc:TJ};const ZC={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class QC{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) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}}const e2="return a * b;";function vJ(e){const{inputs:t,backend:n}=e,{a:s,b:i}=t,o=$n(s.dtype,i.dtype);if(s.dtype==="complex64"){const c=n.texData.get(s.dataId),h=n.texData.get(i.dataId),d=new QC(ZC.REAL,s.shape,i.shape),m=new QC(ZC.IMAG,s.shape,i.shape),f=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:s.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape}],b=n.runWebGLProgram(d,f,"float32"),w=n.runWebGLProgram(m,f,"float32"),L=vc({inputs:{real:b,imag:w},backend:n});return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(w),L}if(n.shouldExecuteOnCPU([s,i])){const c=n.texData.get(s.dataId),h=n.texData.get(i.dataId),[d,m]=F5(s.shape,i.shape,c.values,h.values,o),f=n.makeTensorInfo(m,o),b=n.texData.get(f.dataId);return b.values=d,f}let a;return oe().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new ur(e2,s.shape,i.shape):a=new Wn(e2,s.shape,i.shape),n.runWebGLProgram(a,[s,i],o)}const NJ={kernelName:Oa,backendName:"webgl",kernelFunc:vJ};const CJ={kernelName:Vy,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{ic("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,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),f=o,b=a,w=c;return zp(d,m,f,b,w)}};const RJ=Vp,OJ={kernelName:Od,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{ic("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:h}=n,d=t,m=d.readSync(s.dataId),f=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=RJ(m,f,o,a,c,h);return[b,w]}};const EJ=Gp,DJ={kernelName:Ed,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{ic("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:h}=n,d=t,m=d.readSync(s.dataId),f=d.readSync(i.dataId),b=o,w=a,L=c,x=h,{selectedIndices:v,selectedScores:N}=EJ(m,f,b,w,L,x);return[v,N]}};class kJ{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[h,d]=Aw(s,i,o),m=h.toFixed(3),f=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
vec3 fill = vec3(${n.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - ${m}) * ${c} - (float(y) - ${f}) * ${a};
float coordYFloat = (float(x) - ${m}) * ${a} + (float(y) - ${f}) * ${c};
int coordX = int(round(coordXFloat + ${m}));
int coordY = int(round(coordYFloat + ${f}));
${b}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${i}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}}const FJ={kernelName:Md,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:i,fillValue:o,center:a}=t,c=n,h=new kJ(s.shape,i,o,a),d=c.runWebGLProgram(h,[s],s.dtype);return d}};const _J=GC+`
return sin(x);
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Et({x:r,y:l,width:u,height:p})}toSquare(){let{x:r,y:l,width:u,height:p}=this;const y=Math.abs(u-p);return u<p&&(r-=y/2,u+=y),p<u&&(l-=y/2,p+=y),new Et({x:r,y:l,width:u,height:p})}rescale(r){const l=Cf(r)?r.width:r,u=Cf(r)?r.height:r;return new Et({x:this.x*l,y:this.y*u,width:this.width*l,height:this.height*u})}pad(r,l){let[u,p,y,g]=[this.x-r/2,this.y-l/2,this.width+r,this.height+l];return new Et({x:u,y:p,width:y,height:g})}clipAtImageBorders(r,l){const{x:u,y:p,right:y,bottom:g}=this,I=Math.max(u,0),S=Math.max(p,0),T=y-I,C=g-S,D=Math.min(T,r-I),_=Math.min(C,l-S);return new Et({x:I,y:S,width:D,height:_}).floor()}shift(r,l){const{width:u,height:p}=this,y=this.x+r,g=this.y+l;return new Et({x:y,y:g,width:u,height:p})}padAtBorders(r,l){const u=this.width+1,p=this.height+1;let y=1,g=1,I=u,S=p,T=this.left,C=this.top,D=this.right,_=this.bottom;return D>l&&(I=-D+l+u,D=l),_>r&&(S=-_+r+p,_=r),T<1&&(S=2-T,T=1),C<1&&(S=2-C,C=1),{dy:g,edy:S,dx:y,edx:I,y:C,ey:_,x:T,ex:D,w:u,h:p}}calibrate(r){return 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Reusing existing backend factory.`),!1):(this.registryFactory[r]={factory:l,priority:u},!0)}async setBackend(r){if(this.registryFactory[r]==null)throw new Error(`Backend name '${r}' not found in registry`);if(this.backendName=r,this.registry[r]==null){this.backendInstance=null;const{success:l,asyncInit:u}=this.initializeBackend(r),p=u?await l:l;if(!p)return!1}return this.backendInstance=this.registry[r],this.setupRegisteredKernels(),this.profiler=new WO(this.backendInstance),!0}setupRegisteredKernels(){const r=OI(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=OI(r);l.forEach(u=>{u.disposeFunc!=null&&u.disposeFunc(this.registry[r])})}initializeBackend(r){const l=this.registryFactory[r];if(l==null)throw new Error(`Cannot initialize backend ${r}, no registration found.`);try{const u=l.factory();if(u&&!(u instanceof K2)&&typeof u.then=="function"){const p=++this.pendingBackendInitId,y=u.then(g=>p<this.pendingBackendInitId?!1:(this.registry[r]=g,this.pendingBackendInit=null,!0)).catch(g=>(p<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(g.stack||g.message)),!1));return this.pendingBackendInit=y,{success:y,asyncInit:!0}}else return this.registry[r]=u,{success:!0,asyncInit:!1}}catch(u){return console.warn(`Initialization of backend ${r} failed`),console.warn(u.stack||u.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${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 u=r[l],{success:p,asyncInit:y}=this.initializeBackend(u);if(y||p)return{name:u,asyncInit:y}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(r,l){const u=this.state.tensorInfo.get(l),p=u.backend,y=this.readSync(l);p.disposeData(l),u.backend=r,r.move(l,y,u.shape,u.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(r,l){let u=null;if(l==null){if(typeof r!="function")throw new Error("Please provide a function to tidy()");l=r}else{if(typeof r!="string"&&!(r instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof l!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");u=r}let p;return this.scopedRun(()=>this.startScope(u),()=>this.endScope(p),()=>(p=l(),p instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),p))}scopedRun(r,l,u){r();try{const p=u();return l(),p}catch(p){throw l(),p}}nextTensorId(){return Vu.nextTensorId++}nextVariableId(){return Vu.nextVariableId++}clone(r){const l=this.makeTensorFromDataId(r.dataId,r.shape,r.dtype),u={x:r},p=g=>({x:()=>{const I="float32",S={x:g},T={dtype:I};return H.runKernelFunc(C=>C.cast(g,I),S,null,qc,T)}}),y=[];return this.addTapeNode(this.state.activeScope.name,u,[l],p,y,{}),l}runKernel(r,l,u,p,y){const g=null,I=null;return this.runKernelFunc(g,l,I,r,u,p,y)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,u){const p=this.backend.numDataIds();let y=0;u.forEach(S=>{y+=S.dtype==="complex64"?3:1});const g=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],I=p-l-y-g;if(I>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${I} data ids) after running '${r}'`)}runKernelFunc(r,l,u,p,y,g,I){let S,T=[];const C=this.isTapeOn();p==null&&(p=this.state.activeScope!=null?this.state.activeScope.name:"");const D=this.state.numBytes,_=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let A;const B=Eg(p,this.backendName);let ne;if(B!=null)A=()=>{const P=this.backend.numDataIds();ne=B.kernelFunc({inputs:l,attrs:y,backend:this.backend});const ge=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,P,ge);const ae=ge.map(({dataId:Le,shape:ve,dtype:Ve})=>this.makeTensorFromDataId(Le,ve,Ve));if(C){let Le=this.getTensorsForGradient(p,l,ae);if(Le==null){I==null&&(I=[]);const ve=ae.filter((Ve,at)=>I[at]);Le=(g||[]).slice().concat(ve)}T=this.saveTensorsForBackwardMode(Le)}return ae};else{const P=ge=>{if(!C)return;T=ge.map(ae=>this.keep(this.clone(ae)))};A=()=>{const ge=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,P));const ae=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,ge,ae),ae}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?S=A():(te=this.profiler.profileKernel(p,l,()=>A()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),S=te.outputs)}),C&&this.addTapeNode(p,l,S,u,T,y),this.state.profiling&&this.state.activeProfile.kernels.push({name:p,bytesAdded:this.state.numBytes-D,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-_,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(P=>l[P]!=null?l[P].shape:null),outputShapes:S.map(P=>P.shape),kernelTimeMs:te.timeMs,extraInfo:te.extraInfo}),Array.isArray(ne)?S:S[0]}saveTensorsForBackwardMode(r){const l=r.map(u=>this.keep(this.clone(u)));return l}getTensorsForGradient(r,l,u){const p=RI(r);if(p!=null){const y=p.inputsToSave||[],g=p.outputsToSave||[];let I;p.saveAllInputs?(J(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an array."),I=Object.keys(l).map(T=>l[T])):I=y.map(T=>l[T]);const S=u.filter((T,C)=>g[C]);return I.concat(S)}return null}makeTensor(r,l,u,p){if(r==null)throw new Error("Values passed to engine.makeTensor() are null");u=u||"float32",p=p||this.backend;let y=r;u==="string"&&$u(r[0])&&(y=r.map(S=>_O(S)));const g=p.write(y,l,u),I=new Dn(l,u,g,this.nextTensorId());if(this.incRef(I,p),u==="string"){const S=this.state.tensorInfo.get(g),T=nR(y);this.state.numBytes+=T-S.bytes,S.bytes=T}return I}makeTensorFromDataId(r,l,u,p){u=u||"float32";const y=new Dn(l,u,r,this.nextTensorId());return this.incRef(y,p),y}makeVariable(r,l=!0,u,p){u=u||this.nextVariableId().toString(),p!=null&&p!==r.dtype&&(r=r.cast(p));const y=new Fg(r,l,u,this.nextTensorId());if(this.state.registeredVariables[y.name]!=null)throw new Error(`Variable with name ${y.name} was already registered`);return this.state.registeredVariables[y.name]=y,this.incRef(y,this.backend),y}incRef(r,l){const u=this.state.tensorInfo.has(r.dataId)?this.state.tensorInfo.get(r.dataId).refCount:0;if(this.state.numTensors++,r.dtype==="string"&&this.state.numStringTensors++,u===0){this.state.numDataBuffers++;let p=0;r.dtype!=="complex64"&&r.dtype!=="string"&&(p=r.size*tR(r.dtype)),this.state.tensorInfo.set(r.dataId,{backend:l||this.backend,dtype:r.dtype,shape:r.shape,bytes:p,refCount:0}),this.state.numBytes+=p}this.state.tensorInfo.get(r.dataId).refCount++,r instanceof Fg||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),u=l.refCount;u<=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,u=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(p=>p.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-u;for(const p of this.state.activeProfile.kernels)p.kernelTimeMs=await p.kernelTimeMs,p.extraInfo=await p.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(r,l,u,p,y,g){const I={id:this.state.nextTapeNodeId++,kernelName:r,inputs:l,outputs:u,saved:y},S=RI(r);S!=null&&(p=S.gradFunc),p!=null&&(I.gradient=T=>(T=T.map((C,D)=>{if(C==null){const _=u[D],A=pa(_.size,_.dtype);return this.makeTensor(A,_.shape,_.dtype)}return C}),p(T.length>1?T:T[0],y,g))),this.state.activeTape.push(I)}keep(r){return r.kept=!0,r}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(r){const l={track:[],name:"unnamed scope",id:this.state.nextScopeId++};r&&(l.name=r),this.state.scopeStack.push(l),this.state.activeScope=l}endScope(r){const l=_g(r),u=new Set(l.map(y=>y.id));for(let y=0;y<this.state.activeScope.track.length;y++){const g=this.state.activeScope.track[y];!g.kept&&!u.has(g.id)&&g.dispose()}const p=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],l.forEach(y=>{!y.kept&&y.scopeId===p.id&&this.track(y)})}gradients(r,l,u,p=!1){if(J(l.length>0,()=>"gradients() received an empty list of xs."),u!=null&&u.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${u.dtype}'`);const y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",r));J(y instanceof Dn,()=>"The result y returned by f() must be a tensor.");const g=$O(this.state.activeTape,l,y);if(!p&&g.length===0&&l.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 I={};I[y.id]=u==null?F9(y.shape):u,UO(I,g,T=>this.tidy(T),_9);const S=l.map(T=>I[T.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(T=>{for(const C of T.saved)C.dispose()}),this.state.activeTape=null),{value:y,grads:S}})}customGrad(r){return J(II(r),()=>"The f passed in customGrad(f) must be a function."),(...l)=>{J(l.every(y=>y instanceof Dn),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let u;const p={};return l.forEach((y,g)=>{p[g]=y}),this.runKernelFunc((y,g)=>(u=r(...l,g),J(u.value instanceof Dn,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),J(II(u.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),u.value),p,(y,g)=>{const I=u.gradFunc(y,g),S=Array.isArray(I)?I:[I];J(S.length===l.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(...)."),J(S.every(C=>C instanceof Dn),()=>"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 T={};return S.forEach((C,D)=>{T[D]=()=>C}),T})}}readSync(r){const l=this.state.tensorInfo.get(r);return l.backend.readSync(r)}read(r){const l=this.state.tensorInfo.get(r);return l.backend.read(r)}async time(r){const l=EI(),u=await this.backend.time(r);return u.wallMs=EI()-l,u}track(r){return this.state.activeScope!=null&&(r.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(r)),r}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new KO;for(const r in this.registry)this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}Vu.nextTensorId=0;Vu.nextVariableId=0;function F9(r){const l=Df(Zt(r),"float32");return H.makeTensor(l,r,"float32")}function UI(){const r=AI();if(r._tfengine==null){const l=new rR(r);r._tfengine=new Vu(l)}return aR(r._tfengine.ENV),VO(()=>r._tfengine),r._tfengine}const H=UI();function _9(r,l){const u={a:r,b:l};return H.runKernelFunc((p,y)=>{const g=p.add(r,l);return y([r,l]),g},u,null,Hc)}function XO(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}const Lr=Ds();Lr.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});Lr.registerFlag("IS_BROWSER",()=>XO());Lr.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Lr.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Lr.registerFlag("PROD",()=>!1);Lr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Lr.getBool("DEBUG"));Lr.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Lr.registerFlag("IS_TEST",()=>!1);function Sr(r,l){let u=r;if(Es(r))return l==="string"?[]:[r.length];if(!Array.isArray(r))return[];const p=[];for(;Array.isArray(u)||Es(u)&&l!=="string";)p.push(u.length),u=u[0];return Array.isArray(r)&&Ds().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&JO(r,p,[]),p}function JO(r,l,u){if(u=u||[],!Array.isArray(r)&&!Es(r)){J(l.length===0,()=>`Element arr[${u.join("][")}] is a primitive, but should be an array/TypedArray of ${l[0]} elements`);return}J(l.length>0,()=>`Element arr[${u.join("][")}] should be a primitive, but is an array of ${r.length} elements`),J(r.length===l[0],()=>`Element arr[${u.join("][")}] should have ${l[0]} elements, but has ${r.length} elements`);const p=l.slice(1);for(let y=0;y<r.length;++y)JO(r[y],p,u.concat(y))}function ZO(r,l,u,p){if(r==null)return;if(r!=="numeric"&&r!==l||r==="numeric"&&l==="string")throw new Error(`Argument '${u}' passed to '${p}' must be ${r} tensor, but got ${l} tensor`)}function M(r,l,u,p="numeric"){if(r instanceof Dn)return ZO(p,r.dtype,l,u),r;let y=Uu(r);if(y!=="string"&&["bool","int32","float32"].indexOf(p)>=0&&(y=p),ZO(p,y,l,u),r==null||!Es(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string"){const T=r==null?"null":r.constructor.name;throw new Error(`Argument '${l}' passed to '${u}' must be a Tensor or TensorLike, but got '${T}'`)}const g=Sr(r,y);!Es(r)&&!Array.isArray(r)&&(r=[r]);const I=!0,S=y!=="string"?Dg(r,y):da(r,[],I);return H.makeTensor(S,g,y)}function Wg(r,l,u,p="numeric"){if(!Array.isArray(r))throw new Error(`Argument ${l} passed to ${u} must be a \`Tensor[]\` or \`TensorLike[]\``);const y=r;return y.map((g,I)=>M(g,`${l}[${I}]`,u),p)}const QO="__op";function V(r){const l=Object.keys(r);if(l.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${l.length} keys.`);let u=l[0];const p=r[u];u.endsWith("_")&&(u=u.substring(0,u.length-1)),u=u+QO;const y=(...g)=>{H.startScope(u);try{const I=p(...g);return Ff(I)&&console.error("Cannot return a Promise inside of tidy."),H.endScope(I),I}catch(I){throw H.endScope(null),I}};return Object.defineProperty(y,"name",{value:u,configurable:!0}),y}function W9(r,l){const u=M(r,"real","complex"),p=M(l,"imag","complex");tn(u.shape,p.shape,`real and imag shapes, ${u.shape} and ${p.shape}, must match in call to tf.complex().`);const y=I=>I.complex(u,p),g={real:u,imag:p};return H.runKernelFunc(y,g,null,TR)}const zi=V({complex_:W9});function Vi(r,l,u,p){if(p==null&&(p=Uu(r)),p==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!Es(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(l!=null){kf(l);const y=Zt(l),g=Zt(u);J(y===g,()=>`Based on the provided shape, [${l}], the tensor should have ${y} values but has ${g}`);for(let I=0;I<u.length;++I){const S=u[I],T=I===u.length-1?S!==Zt(l.slice(I)):!0;J(u[I]===l[I]||!T,()=>`Error creating a new Tensor. Inferred shape (${u}) does not match the provided shape (${l}). `)}}return!Es(r)&&!Array.isArray(r)&&(r=[r]),l=l||u,r=p!=="string"?Dg(r,p):da(r,[],!0),H.makeTensor(r,l,p)}function BI(r,l,u){const p=Sr(r,u);return Vi(r,l,p,u)}function Gu(r,l="float32",u){return l=l||"float32",kf(r),new zO(r,l,u)}function $9(r,l){const u=M(r,"x","cast");if(!eR(l))throw new Error(`Failed to cast to unknown dtype ${l}`);if(l==="string"&&u.dtype!=="string"||l!=="string"&&u.dtype==="string")throw new Error("Only strings can be casted to strings");const p={x:u},y={dtype:l};return H.runKernelFunc(g=>g.cast(u,l),p,null,qc,y)}const Ie=V({cast_:$9});function U9(r){const l=M(r,"x","clone",null),u=()=>H.makeTensorFromDataId(l.dataId,l.shape,l.dtype),p={x:l};return H.runKernelFunc(u,p,null,Jf)}const gi=V({clone_:U9});function MI(r,l=!1){console.log(r.toString(l))}UI();const B9={buffer:Gu,cast:Ie,clone:gi,print:MI};GO(B9);function M9(r,l){const u=M(r,"x","reshape",null),p={x:u},y={shape:l},g=(I,S)=>(l=X2(l,u.size),J(u.size===Zt(l),()=>"new shape and old shape must have the same number of elements."),S([u]),I.reshape(u,l));return H.runKernelFunc(g,p,null,lg,y)}const re=V({reshape_:M9});function P9(r,l,u=!1,p=!1){let y=M(r,"a","matMul"),g=M(l,"b","matMul");[y,g]=St(y,g);const I=(C,D)=>{D([y,g]);const _=u?y.shape[y.rank-2]:y.shape[y.rank-1],A=p?g.shape[g.rank-1]:g.shape[g.rank-2],B=u?y.shape[y.rank-1]:y.shape[y.rank-2],ne=p?g.shape[g.rank-2]:g.shape[g.rank-1],te=y.shape.slice(0,-2),P=g.shape.slice(0,-2),ge=Zt(te),ae=Zt(P),Le=ge===ae||ge===1||ae===1;J(y.rank>=2&&g.rank>=2&&Le,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${te}) and (${P}).`),J(_===A,()=>`Error in matMul: inner shapes (${_}) and (${A}) of Tensors with shapes ${y.shape} and ${g.shape} and transposeA=${u} and transposeB=${p} must match.`);const ve=ge>ae?te:P,Ve=ve.concat([B,ne]),at=u?re(y,[ge,_,B]):re(y,[ge,B,_]),pt=p?re(g,[ae,ne,A]):re(g,[ae,A,ne]),$t=C.batchMatMul(at,pt,u,p);return re($t,Ve)},S={a:y,b:g},T={transposeA:u,transposeB:p};return H.runKernelFunc(I,S,null,Wf,T)}const yn=V({matMul_:P9});function z9(r,l){const u=M(r,"x","transpose");if(l==null&&(l=u.shape.map((g,I)=>I).reverse()),J(u.rank===l.length,()=>`Error in transpose: rank of input ${u.rank} must match length of perm ${l}.`),l.forEach(g=>{J(g>=0&&g<u.rank,()=>`All entries in 'perm' must be between 0 and ${u.rank-1} but got ${l}`)}),u.rank<=1)return u.clone();const p={x:u},y={perm:l};return H.runKernelFunc(g=>g.transpose(u,l),p,null,vg,y)}const xn=V({transpose_:z9});function PI(r,l,u){if(Gc(r),l!=null&&l.length!==3)throw new Error("tensor3d() requires shape to have three numbers");const p=Sr(r,u);if(p.length!==3&&p.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(p.length===1&&l==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Vi(r,l,p,u)}const zI={};vu(zI,{fromPixels:()=>Y9,toPixels:()=>G9});let Kc;function V9(r,l=3){if(l>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let u=!1,p=!1,y=!1,g=!1,I=!1;if(r.data instanceof Uint8Array)u=!0;else if(typeof ImageData!="undefined"&&r instanceof ImageData)p=!0;else if(typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement)y=!0;else if(typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement)g=!0;else if(r.getContext!=null)I=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r.constructor.name}`);if(y){const B=2;if(y&&r.readyState<B)throw new Error("The video element has not loaded data yet. 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I=M(r,"boxes","nonMaxSuppression"),S=M(l,"scores","nonMaxSuppression"),T=Ks(I,S,u,p,y,g);u=T.maxOutputSize,p=T.iouThreshold,y=T.scoreThreshold,g=T.softNmsSigma;const C={boxes:I,scores:S},D={maxOutputSize:u,iouThreshold:p,scoreThreshold:y,softNmsSigma:g},_=H.runKernel(mO,C,D);return{selectedIndices:_[0],selectedScores:_[1]}}const N1=V({nonMaxSuppressionWithScore_:RQ});async function OQ(r,l,u,p=.5,y=Number.NEGATIVE_INFINITY,g=0){const I=M(r,"boxes","nonMaxSuppressionAsync"),S=M(l,"scores","nonMaxSuppressionAsync"),T=Ks(I,S,u,p,y,g);u=T.maxOutputSize,p=T.iouThreshold,y=T.scoreThreshold,g=T.softNmsSigma;const C=await Promise.all([I.data(),S.data()]),D=C[0],_=C[1],A=T1(D,_,u,p,y,g);return I!==r&&I.dispose(),S!==l&&S.dispose(),A}const C1=OQ;function EQ(r,l,u,p=.5,y=Number.NEGATIVE_INFINITY,g=!1){const I=M(r,"boxes","nonMaxSuppression"),S=M(l,"scores","nonMaxSuppression"),T=Ks(I,S,u,p,y,null),C=T.maxOutputSize,D=T.iouThreshold,_=T.scoreThreshold,A={boxes:I,scores:S},B={maxOutputSize:C,iouThreshold:D,scoreThreshold:_,padToMaxOutputSize:g},ne=H.runKernel(pO,A,B);return{selectedIndices:ne[0],validOutputs:ne[1]}}const R1=V({nonMaxSuppressionPadded_:EQ});async function DQ(r,l,u,p=.5,y=Number.NEGATIVE_INFINITY,g=!1){const I=M(r,"boxes","nonMaxSuppressionAsync"),S=M(l,"scores","nonMaxSuppressionAsync"),T=Ks(I,S,u,p,y,null),C=T.maxOutputSize,D=T.iouThreshold,_=T.scoreThreshold,[A,B]=await Promise.all([I.data(),S.data()]),ne=x1(A,B,C,D,_,g);return I!==r&&I.dispose(),S!==l&&S.dispose(),ne}const O1=DQ;function kQ(r,l,u=!1){const p=M(r,"images","resizeBilinear");J(p.rank===3||p.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${p.rank}.`),J(l.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${l}.`);let y=p,g=!1;p.rank===3&&(g=!0,y=re(p,[1,p.shape[0],p.shape[1],p.shape[2]]));const[I,S]=l,T=(A,B)=>(B([y]),A.resizeBilinear(y,I,S,u)),C={images:y},D={alignCorners:u,size:l},_=H.runKernelFunc(T,C,null,ug,D);return g?re(_,[_.shape[1],_.shape[2],_.shape[3]]):_}const E1=V({resizeBilinear_:kQ});function FQ(r,l,u=!1){const p=M(r,"images","resizeNearestNeighbor");J(p.rank===3||p.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${p.rank}.`),J(l.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${l}.`),J(p.dtype==="float32"||p.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let y=p,g=!1;p.rank===3&&(g=!0,y=re(p,[1,p.shape[0],p.shape[1],p.shape[2]]));const[I,S]=l,T={images:y},C={alignCorners:u,size:l},D=(A,B)=>(B([y]),A.resizeNearestNeighbor(y,I,S,u)),_=H.runKernelFunc(D,T,null,hg,C);return g?re(_,[_.shape[1],_.shape[2],_.shape[3]]):_}const D1=V({resizeNearestNeighbor_:FQ});function 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_=T,A=C,B=D,ne=!1;C.rank===4&&(ne=!0,_=re(T,[1,T.shape[0],T.shape[1],T.shape[2],T.shape[3]]),A=re(C,[1,C.shape[0],C.shape[1],C.shape[2],C.shape[3]]),B=re(D,[1,D.shape[0],D.shape[1],D.shape[2],D.shape[3]])),J(_.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${_.rank}.`),J(A.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${A.rank}.`),J(B.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${B.rank}.`),J(yo(y,g),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. 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eD={kernelName:lO,inputsToSave:["x"],gradFunc:(r,l,u)=>{const p=l[0],{paddings:y}=u,g=y.map(I=>I[0]);return{x:()=>Tt(r,g,p.shape)}}};const tD={kernelName:hO,inputsToSave:["a","b"],gradFunc:(r,l)=>{const[u,p]=l,y=ot(u.shape,p.shape),g=()=>{const S=Dt(u.shape,y);return S.length>0?re(_e(r,S),u.shape):r},I=()=>{const S=le(r,It(QI(Me(u,p)))),T=Dt(p.shape,y);return T.length>0?re(_e(S,T),p.shape):S};return{a:g,b:I}}};const nD={kernelName:ig,inputsToSave:["a","b"],gradFunc:(r,l)=>{const[u,p]=l,y=ot(u.shape,p.shape),g=()=>{const S=le(r,Ie(p,"float32")),T=Dt(u.shape,y);return T.length>0?re(_e(S,T),u.shape):S},I=()=>{const S=le(r,Ie(u,"float32")),T=Dt(p.shape,y);return T.length>0?re(_e(S,T),p.shape):S};return{a:g,b:I}}};const sD={kernelName:rg,gradFunc:r=>({x:()=>It(r)})};const iD={kernelName:gO,inputsToSave:["indices"],gradFunc:(r,l)=>{const u=l[0];return{indices:()=>Fs(u.shape,"float32")}}};const rD={kernelName:fO,gradFunc:r=>({x:()=>Xe(r)})};const Lx={kernelName:og,inputsToSave:["x"],gradFunc:(r,l,u)=>{const p=l[0],{paddings:y}=u,g=y.map(I=>I[0]);return{x:()=>Tt(r,g,p.shape)}}};const oD={kernelName:ag,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(r,l)=>{const[u,p,y]=l,g=u,I=p,S=ot(g.shape,I.shape),T=()=>{const D=Ie(I,"float32");let _=le(r,le(D,ba(g,Be(D,Fe(1)))));const A=Dt(g.shape,S);return A.length>0&&(_=_e(_,A)),re(_,g.shape)},C=()=>{const D=bi(g,0),_=Gn(D,bo(g),Xe(g));let A=le(r,le(y,_));const B=Dt(I.shape,S);return B.length>0&&(A=_e(A,B)),re(A,I.shape)};return{a:T,b:C}}};const aD={kernelName:yO,inputsToSave:["x","alpha"],gradFunc:(r,l)=>{const[u,p]=l,y=bi(u,0);return{x:()=>Gn(y,r,le(r,p)),alpha:()=>{let g=Gn(y,Xe(r),le(r,u));const I=Dt(p.shape,r.shape);return I.length>0&&(g=_e(g,I)),re(g,p.shape)}}}};const cD={kernelName:LO,inputsToSave:["x"],gradFunc:(r,l)=>{const[u]=l;return{x:()=>Me(r,It(yt(u)))}}};const 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Hn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const ey=Ze(Qe());function sl(r,l){return function(u,p,y,g){const I=ey.tensor4d(r(u*p*y*y),[y,y,u,p]),S=ey.tensor1d(r(p));return l.push({paramPath:`${g}/filters`},{paramPath:`${g}/bias`}),{filters:I,bias:S}}}const ty=Ze(Qe());function ny(r,l){return function(u,p,y){const g=ty.tensor2d(r(u*p),[u,p]),I=ty.tensor1d(r(p));return l.push({paramPath:`${y}/weights`},{paramPath:`${y}/bias`}),{weights:g,bias:I}}}class xx{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const Qu=Ze(Qe());function il(r,l){return function(u,p,y){const g=Qu.tensor4d(r(3*3*u),[3,3,u,1]),I=Qu.tensor4d(r(u*p),[1,1,u,p]),S=Qu.tensor1d(r(p));return l.push({paramPath:`${y}/depthwise_filter`},{paramPath:`${y}/pointwise_filter`},{paramPath:`${y}/bias`}),new xx(g,I,S)}}function rl(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),y=r(`${l}/bias`,1);return new xx(u,p,y)}}function gs(r,l){return function(u,p,y){const g=r[u];if(!la(g,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${g}`);return l.push({originalPath:u,paramPath:y||u}),g}}function qn(r){let l=r;function u(y){const g=l.slice(0,y);return l=l.slice(y),g}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function sy(r,l){const u=sl(r,l),p=il(r,l);function y(I,S,T,C=!1){const D=C?u(I,S,3,`${T}/conv0`):p(I,S,`${T}/conv0`),_=p(S,S,`${T}/conv1`),A=p(S,S,`${T}/conv2`);return{conv0:D,conv1:_,conv2:A}}function g(I,S,T,C=!1){const{conv0:D,conv1:_,conv2:A}=y(I,S,T,C),B=p(S,S,`${T}/conv3`);return{conv0:D,conv1:_,conv2:A,conv3:B}}return{extractDenseBlock3Params:y,extractDenseBlock4Params:g}}function MD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=qn(r),{extractDenseBlock4Params:y}=sy(u,l),g=y(3,32,"dense0",!0),I=y(32,64,"dense1"),S=y(64,128,"dense2"),T=y(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:g,dense1:I,dense2:S,dense3:T}}}function iy(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function ry(r,l){const u=gs(r,l),p=iy(u),y=rl(u);function g(S,T=!1){const C=T?p(`${S}/conv0`):y(`${S}/conv0`),D=y(`${S}/conv1`),_=y(`${S}/conv2`);return{conv0:C,conv1:D,conv2:_}}function I(S,T=!1){const C=T?p(`${S}/conv0`):y(`${S}/conv0`),D=y(`${S}/conv1`),_=y(`${S}/conv2`),A=y(`${S}/conv3`);return{conv0:C,conv1:D,conv2:_,conv3:A}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:I}}function PD(r){const l=[],{extractDenseBlock4Params:u}=ry(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return Hn(r,l),{params:p,paramMappings:l}}const Io=Ze(Qe());class oy extends En{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return Io.tidy(()=>{const u=Io.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],y=Hs(u,p).div(Io.scalar(255));let g=Zu(y,l.dense0,!0);return g=Zu(g,l.dense1),g=Zu(g,l.dense2),g=Zu(g,l.dense3),g=Io.avgPool(g,[7,7],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Ot(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return PD(r)}extractParams(r){return MD(r)}}const ol=Ze(Qe());function ed(r,l){return ol.tidy(()=>ol.add(ol.matMul(r,l.weights),l.bias))}function zD(r,l,u){const p=[],{extractWeights:y,getRemainingWeights:g}=qn(r),I=ny(y,p),S=I(l,u,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:p,params:{fc:S}}}function VD(r){const l=[],u=gs(r,l);function p(g){const I=u(`${g}/weights`,2),S=u(`${g}/bias`,1);return{weights:I,bias:S}}const y={fc:p("fc")};return Hn(r,l),{params:y,paramMappings:l}}function ay(r){const l={},u={};return Object.keys(r).forEach(p=>{const y=p.startsWith("fc")?u:l;y[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const GD=Ze(Qe());class cy extends En{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return GD.tidy(()=>{const u=r instanceof yr?this.faceFeatureExtractor.forwardInput(r):r;return ed(u.as2D(u.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return zD(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=ay(r);return this.faceFeatureExtractor.loadFromWeightMap(l),VD(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,y=r.slice(0,r.length-p),g=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(y),this.extractClassifierParams(g)}}const yf=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class co{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);yf.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return yf.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const al=Ze(Qe());class bf extends cy{constructor(r=new oy){super("FaceExpressionNet",r)}forwardInput(r){return al.tidy(()=>al.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Ot(r))}async predictExpressions(r){const l=await Ot(r),u=await this.forwardInput(l),p=await Promise.all(al.unstack(u).map(async g=>{const I=await g.data();return g.dispose(),I}));u.dispose();const y=p.map(g=>new co(g));return l.isBatchInput?y:y[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function ff(r){return r.expressions instanceof co}function Ou(r,l){const u={expressions:l};return Object.assign({},r,u)}function see(r,l,u=.1,p){const y=Array.isArray(l)?l:[l];y.forEach(g=>{const I=g instanceof co?g:ff(g)?g.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),T=S.filter(_=>_.probability>u),C=mi(g)?g.detection.box.bottomLeft:p||new Ke(0,0),D=new Vc(T.map(_=>`${_.expression} (${ha(_.probability)})`),C);D.draw(r)})}function oo(r){return mi(r)&&r.landmarks instanceof Rs&&r.unshiftedLandmarks instanceof Rs&&r.alignedRect instanceof Vt}function ia(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),y=p.align(),{imageDims:g}=r.detection,I=new Vt(r.detection.score,y.rescale(g.reverse()),g),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class YD{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:y,pointSize:g,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=g||2,this.lineColor=y||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class HD{constructor(r,l={}){this.faceLandmarks=r,this.options=new YD(l)}draw(r){const l=zn(r),{drawLines:u,drawPoints:p,lineWidth:y,lineColor:g,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof Mc&&(l.strokeStyle=g,l.lineWidth=y,br(l,this.faceLandmarks.getJawOutline()),br(l,this.faceLandmarks.getLeftEyeBrow()),br(l,this.faceLandmarks.getRightEyeBrow()),br(l,this.faceLandmarks.getNose()),br(l,this.faceLandmarks.getLeftEye(),!0),br(l,this.faceLandmarks.getRightEye(),!0),br(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const T=C=>{l.beginPath(),l.arc(C.x,C.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(T)}}}function iee(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const y=p instanceof Rs?p:oo(p)?p.landmarks:void 0;if(!y)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new HD(y).draw(r)})}const Qm={};vu(Qm,{AnchorPosition:()=>Mi,DrawBox:()=>SI,DrawBoxOptions:()=>j2,DrawFaceLandmarks:()=>HD,DrawFaceLandmarksOptions:()=>YD,DrawTextField:()=>Vc,DrawTextFieldOptions:()=>Of,drawContour:()=>br,drawDetections:()=>y9,drawFaceExpressions:()=>see,drawFaceLandmarks:()=>iee});function ree(r,l){const u=sl(r,l),p=il(r,l);function y(I,S,T){const C=p(I,S,`${T}/separable_conv0`),D=p(S,S,`${T}/separable_conv1`),_=u(I,S,1,`${T}/expansion_conv`);return{separable_conv0:C,separable_conv1:D,expansion_conv:_}}function g(I,S){const T=p(I,I,`${S}/separable_conv0`),C=p(I,I,`${S}/separable_conv1`),D=p(I,I,`${S}/separable_conv2`);return{separable_conv0:T,separable_conv1:C,separable_conv2:D}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:y,extractMainBlockParams:g}}function qD(r,l){const u=[],{extractWeights:p,getRemainingWeights:y}=qn(r),{extractConvParams:g,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:T}=ree(p,u),C=g(3,32,3,"entry_flow/conv_in"),D=S(32,64,"entry_flow/reduction_block_0"),_=S(64,128,"entry_flow/reduction_block_1"),A={conv_in:C,reduction_block_0:D,reduction_block_1:_},B={};Ui(l,0,1).forEach(ge=>{B[`main_block_${ge}`]=T(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),P={reduction_block:ne,separable_conv:te};if(y().length!==0)throw new Error(`weights remaing after extract: ${y().length}`);return{paramMappings:u,params:{entry_flow:A,middle_flow:B,exit_flow:P}}}function oee(r,l){const u=gs(r,l),p=iy(u),y=rl(u);function g(S){const T=y(`${S}/separable_conv0`),C=y(`${S}/separable_conv1`),D=p(`${S}/expansion_conv`);return{separable_conv0:T,separable_conv1:C,expansion_conv:D}}function I(S){const T=y(`${S}/separable_conv0`),C=y(`${S}/separable_conv1`),D=y(`${S}/separable_conv2`);return{separable_conv0:T,separable_conv1:C,separable_conv2:D}}return{extractConvParams:p,extractSeparableConvParams:y,extractReductionBlockParams:g,extractMainBlockParams:I}}function jD(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:y,extractReductionBlockParams:g,extractMainBlockParams:I}=oee(r,u),S=p("entry_flow/conv_in"),T=g("entry_flow/reduction_block_0"),C=g("entry_flow/reduction_block_1"),D={conv_in:S,reduction_block_0:T,reduction_block_1:C},_={};Ui(l,0,1).forEach(te=>{_[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const A=g("exit_flow/reduction_block"),B=y("exit_flow/separable_conv"),ne={reduction_block:A,separable_conv:B};return Hn(r,u),{params:{entry_flow:D,middle_flow:_,exit_flow:ne},paramMappings:u}}const on=Ze(Qe());function KD(r,l,u){return on.add(on.conv2d(r,l.filters,u,"same"),l.bias)}function Tx(r,l,u=!0){let p=u?on.relu(r):r;return p=as(p,l.separable_conv0,[1,1]),p=as(on.relu(p),l.separable_conv1,[1,1]),p=on.maxPool(p,[3,3],[2,2],"same"),p=on.add(p,KD(r,l.expansion_conv,[2,2])),p}function aee(r,l){let u=as(on.relu(r),l.separable_conv0,[1,1]);return u=as(on.relu(u),l.separable_conv1,[1,1]),u=as(on.relu(u),l.separable_conv2,[1,1]),u=on.add(u,r),u}class XD extends En{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return on.tidy(()=>{const u=on.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],y=Hs(u,p).div(on.scalar(256));let g=on.relu(KD(y,l.entry_flow.conv_in,[2,2]));return g=Tx(g,l.entry_flow.reduction_block_0,!1),g=Tx(g,l.entry_flow.reduction_block_1),Ui(this._numMainBlocks,0,1).forEach(I=>{g=aee(g,l.middle_flow[`main_block_${I}`])}),g=Tx(g,l.exit_flow.reduction_block),g=on.relu(as(g,l.exit_flow.separable_conv,[1,1])),g})}async forward(r){return this.forwardInput(await Ot(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return jD(r,this._numMainBlocks)}extractParams(r){return qD(r,this._numMainBlocks)}}function JD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=qn(r),y=ny(u,l),g=y(512,1,"fc/age"),I=y(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:g,gender:I}}}}function ZD(r){const l=[],u=gs(r,l);function p(g){const I=u(`${g}/weights`,2),S=u(`${g}/bias`,1);return{weights:I,bias:S}}const y={fc:{age:p("fc/age"),gender:p("fc/gender")}};return Hn(r,l),{params:y,paramMappings:l}}var $i;(function(r){r.FEMALE="female",r.MALE="male"})($i||($i={}));const Hi=Ze(Qe());class vf extends En{constructor(r=new XD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Hi.tidy(()=>{const u=r instanceof yr?this.faceFeatureExtractor.forwardInput(r):r,p=Hi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),y=ed(p,l.fc.age).as1D(),g=ed(p,l.fc.gender);return{age:y,gender:g}})}forwardInput(r){return Hi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Hi.softmax(u)}})}async forward(r){return this.forwardInput(await Ot(r))}async predictAgeAndGender(r){const l=await Ot(r),u=await this.forwardInput(l),p=Hi.unstack(u.age),y=Hi.unstack(u.gender),g=p.map((S,T)=>({ageTensor:S,genderTensor:y[T]})),I=await Promise.all(g.map(async({ageTensor:S,genderTensor:T})=>{const C=(await S.data())[0],D=(await T.data())[0],_=D>.5,A=_?$i.MALE:$i.FEMALE,B=_?D:1-D;return S.dispose(),T.dispose(),{age:C,gender:A,genderProbability:B}}));return u.age.dispose(),u.gender.dispose(),l.isBatchInput?I:I[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return JD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=ay(r);return this.faceFeatureExtractor.loadFromWeightMap(l),ZD(u)}extractParams(r){const l=512*1+1+(512*2+2),u=r.slice(0,r.length-l),p=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(u),this.extractClassifierParams(p)}}const ys=Ze(Qe());class ly extends cy{postProcess(r,l,u){const p=u.map(({width:g,height:I})=>{const S=l/Math.max(I,g);return{width:g*S,height:I*S}}),y=p.length;return ys.tidy(()=>{const g=(D,_)=>ys.stack([ys.fill([68],D,"float32"),ys.fill([68],_,"float32")],1).as2D(1,136).as1D(),I=(D,_)=>{const{width:A,height:B}=p[D];return _(A,B)?Math.abs(A-B)/2:0},S=D=>I(D,(_,A)=>_<A),T=D=>I(D,(_,A)=>A<_),C=r.mul(ys.fill([y,136],l,"float32")).sub(ys.stack(Array.from(Array(y),(D,_)=>g(S(_),T(_))))).div(ys.stack(Array.from(Array(y),(D,_)=>g(p[_].width,p[_].height))));return C})}forwardInput(r){return ys.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([u,p])=>({height:u,width:p})))})}async forward(r){return this.forwardInput(await Ot(r))}async detectLandmarks(r){const l=await Ot(r),u=ys.tidy(()=>ys.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(y,g)=>{const I=Array.from(await y.data()),S=I.filter((C,D)=>Nf(D)),T=I.filter((C,D)=>!Nf(D));return new Mc(Array(68).fill(0).map((C,D)=>new Ke(S[D],T[D])),{height:l.getInputHeight(g),width:l.getInputWidth(g)})}));return u.forEach(y=>y.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class Wc extends ly{constructor(r=new oy){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function QD(r){const l=[],{extractDenseBlock3Params:u}=ry(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return Hn(r,l),{params:p,paramMappings:l}}function ek(r){const l=[],{extractWeights:u,getRemainingWeights:p}=qn(r),{extractDenseBlock3Params:y}=sy(u,l),g=y(3,32,"dense0",!0),I=y(32,64,"dense1"),S=y(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:g,dense1:I,dense2:S}}}const xo=Ze(Qe());class tk extends En{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return xo.tidy(()=>{const u=xo.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],y=Hs(u,p).div(xo.scalar(255));let g=Qg(y,l.dense0,!0);return g=Qg(g,l.dense1),g=Qg(g,l.dense2),g=xo.avgPool(g,[14,14],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Ot(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return QD(r)}extractParams(r){return ek(r)}}class gf extends ly{constructor(r=new tk){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class M2 extends Wc{}const hy=Ze(Qe());function nk(r,l){return hy.add(hy.mul(r,l.weights),l.biases)}const cl=Ze(Qe());function Ax(r,l,u,p,y="same"){const{filters:g,bias:I}=l.conv;let S=cl.conv2d(r,g,u,y);return S=cl.add(S,I),S=nk(S,l.scale),p?cl.relu(S):S}function sk(r,l){return Ax(r,l,[1,1],!0)}function vx(r,l){return Ax(r,l,[1,1],!1)}function uy(r,l){return Ax(r,l,[2,2],!0,"valid")}const bs=Ze(Qe());function cee(r,l){function u(S,T,C){const D=r(S),_=D.length/(T*C*C);if(pI(_))throw new Error(`depth has to be an integer: ${_}, weights.length: ${D.length}, numFilters: ${T}, filterSize: ${C}`);return bs.tidy(()=>bs.transpose(bs.tensor4d(D,[T,_,C,C]),[2,3,1,0]))}function p(S,T,C,D){const _=u(S,T,C),A=bs.tensor1d(r(T));return l.push({paramPath:`${D}/filters`},{paramPath:`${D}/bias`}),{filters:_,bias:A}}function y(S,T){const C=bs.tensor1d(r(S)),D=bs.tensor1d(r(S));return l.push({paramPath:`${T}/weights`},{paramPath:`${T}/biases`}),{weights:C,biases:D}}function g(S,T,C,D){const _=p(S,T,C,`${D}/conv`),A=y(T,`${D}/scale`);return{conv:_,scale:A}}function I(S,T,C,D,_=!1){const A=g((_?.5:1)*S,T,C,`${D}/conv1`),B=g(S,T,C,`${D}/conv2`);return{conv1:A,conv2:B}}return{extractConvLayerParams:g,extractResidualLayerParams:I}}function ik(r){const{extractWeights:l,getRemainingWeights:u}=qn(r),p=[],{extractConvLayerParams:y,extractResidualLayerParams:g}=cee(l,p),I=y(4704,32,7,"conv32_down"),S=g(9216,32,3,"conv32_1"),T=g(9216,32,3,"conv32_2"),C=g(9216,32,3,"conv32_3"),D=g(36864,64,3,"conv64_down",!0),_=g(36864,64,3,"conv64_1"),A=g(36864,64,3,"conv64_2"),B=g(36864,64,3,"conv64_3"),ne=g(147456,128,3,"conv128_down",!0),te=g(147456,128,3,"conv128_1"),P=g(147456,128,3,"conv128_2"),ge=g(589824,256,3,"conv256_down",!0),ae=g(589824,256,3,"conv256_1"),Le=g(589824,256,3,"conv256_2"),ve=g(589824,256,3,"conv256_down_out"),Ve=bs.tidy(()=>bs.transpose(bs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const at={conv32_down:I,conv32_1:S,conv32_2:T,conv32_3:C,conv64_down:D,conv64_1:_,conv64_2:A,conv64_3:B,conv128_down:ne,conv128_1:te,conv128_2:P,conv256_down:ge,conv256_1:ae,conv256_2:Le,conv256_down_out:ve,fc:Ve};return{params:at,paramMappings:p}}function lee(r,l){const u=gs(r,l);function p(I){const S=u(`${I}/scale/weights`,1),T=u(`${I}/scale/biases`,1);return{weights:S,biases:T}}function y(I){const S=u(`${I}/conv/filters`,4),T=u(`${I}/conv/bias`,1),C=p(I);return{conv:{filters:S,bias:T},scale:C}}function g(I){return{conv1:y(`${I}/conv1`),conv2:y(`${I}/conv2`)}}return{extractConvLayerParams:y,extractResidualLayerParams:g}}function rk(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=lee(r,l),y=u("conv32_down"),g=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),T=p("conv64_down"),C=p("conv64_1"),D=p("conv64_2"),_=p("conv64_3"),A=p("conv128_down"),B=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),P=p("conv256_1"),ge=p("conv256_2"),ae=p("conv256_down_out"),Le=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!dI(Le))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Le}`);const ve={conv32_down:y,conv32_1:g,conv32_2:I,conv32_3:S,conv64_down:T,conv64_1:C,conv64_2:D,conv64_3:_,conv128_down:A,conv128_1:B,conv128_2:ne,conv256_down:te,conv256_1:P,conv256_2:ge,conv256_down_out:ae,fc:Le};return Hn(r,l),{params:ve,paramMappings:l}}const jn=Ze(Qe());function wi(r,l){let u=sk(r,l.conv1);return u=vx(u,l.conv2),u=jn.add(u,r),u=jn.relu(u),u}function td(r,l){let u=uy(r,l.conv1);u=vx(u,l.conv2);let p=jn.avgPool(r,2,2,"valid");const y=jn.zeros(p.shape),g=p.shape[3]!==u.shape[3],I=p.shape[1]!==u.shape[1]||p.shape[2]!==u.shape[2];if(I){const S=[...u.shape];S[1]=1;const T=jn.zeros(S);u=jn.concat([u,T],1);const C=[...u.shape];C[2]=1;const D=jn.zeros(C);u=jn.concat([u,D],2)}return p=g?jn.concat([p,y],3):p,u=jn.add(p,u),u=jn.relu(u),u}const _s=Ze(Qe());class _c extends En{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return _s.tidy(()=>{const u=_s.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],y=Hs(u,p).div(_s.scalar(256));let g=uy(y,l.conv32_down);g=_s.maxPool(g,3,2,"valid"),g=wi(g,l.conv32_1),g=wi(g,l.conv32_2),g=wi(g,l.conv32_3),g=td(g,l.conv64_down),g=wi(g,l.conv64_1),g=wi(g,l.conv64_2),g=wi(g,l.conv64_3),g=td(g,l.conv128_down),g=wi(g,l.conv128_1),g=wi(g,l.conv128_2),g=td(g,l.conv256_down),g=wi(g,l.conv256_1),g=wi(g,l.conv256_2),g=td(g,l.conv256_down_out);const I=g.mean([1,2]),S=_s.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Ot(r))}async computeFaceDescriptor(r){const l=await Ot(r),u=_s.tidy(()=>_s.unstack(this.forwardInput(l))),p=await Promise.all(u.map(y=>y.data()));return u.forEach(y=>y.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return rk(r)}extractParams(r){return ik(r)}}function B2(r){const l=new _c;return l.extractWeights(r),l}function Eu(r,l){const u={descriptor:l};return Object.assign({},r,u)}function U2(r){return typeof r.age=="number"}function Du(r,l){const u={age:l};return Object.assign({},r,u)}function $2(r){return(r.gender===$i.MALE||r.gender===$i.FEMALE)&&zc(r.genderProbability)}function Ru(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const Li=Ze(Qe());function hee(r,l){function u(T,C){const D=Li.tensor4d(r(3*3*T),[3,3,T,1]),_=Li.tensor1d(r(T)),A=Li.tensor1d(r(T)),B=Li.tensor1d(r(T)),ne=Li.tensor1d(r(T));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/batch_norm_scale`},{paramPath:`${C}/batch_norm_offset`},{paramPath:`${C}/batch_norm_mean`},{paramPath:`${C}/batch_norm_variance`}),{filters:D,batch_norm_scale:_,batch_norm_offset:A,batch_norm_mean:B,batch_norm_variance:ne}}function p(T,C,D,_,A){const B=Li.tensor4d(r(T*C*D*D),[D,D,T,C]),ne=Li.tensor1d(r(C));return l.push({paramPath:`${_}/filters`},{paramPath:`${_}/${A?"batch_norm_offset":"bias"}`}),{filters:B,bias:ne}}function y(T,C,D,_){const{filters:A,bias:B}=p(T,C,D,_,!0);return{filters:A,batch_norm_offset:B}}function g(T,C,D){const _=u(T,`${D}/depthwise_conv`),A=y(T,C,1,`${D}/pointwise_conv`);return{depthwise_conv:_,pointwise_conv:A}}function I(){const T=y(3,32,3,"mobilenetv1/conv_0"),C=g(32,64,"mobilenetv1/conv_1"),D=g(64,128,"mobilenetv1/conv_2"),_=g(128,128,"mobilenetv1/conv_3"),A=g(128,256,"mobilenetv1/conv_4"),B=g(256,256,"mobilenetv1/conv_5"),ne=g(256,512,"mobilenetv1/conv_6"),te=g(512,512,"mobilenetv1/conv_7"),P=g(512,512,"mobilenetv1/conv_8"),ge=g(512,512,"mobilenetv1/conv_9"),ae=g(512,512,"mobilenetv1/conv_10"),Le=g(512,512,"mobilenetv1/conv_11"),ve=g(512,1024,"mobilenetv1/conv_12"),Ve=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:T,conv_1:C,conv_2:D,conv_3:_,conv_4:A,conv_5:B,conv_6:ne,conv_7:te,conv_8:P,conv_9:ge,conv_10:ae,conv_11:Le,conv_12:ve,conv_13:Ve}}function S(){const T=y(1024,256,1,"prediction_layer/conv_0"),C=y(256,512,3,"prediction_layer/conv_1"),D=y(512,128,1,"prediction_layer/conv_2"),_=y(128,256,3,"prediction_layer/conv_3"),A=y(256,128,1,"prediction_layer/conv_4"),B=y(128,256,3,"prediction_layer/conv_5"),ne=y(256,64,1,"prediction_layer/conv_6"),te=y(64,128,3,"prediction_layer/conv_7"),P=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),ae=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Le=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),ve=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),at=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),pt=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Gt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),qe=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),ln=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),bt={box_encoding_predictor:P,class_predictor:ge},ws={box_encoding_predictor:ae,class_predictor:Le},Cr={box_encoding_predictor:ve,class_predictor:Ve},Rr={box_encoding_predictor:at,class_predictor:pt},Ta={box_encoding_predictor:$t,class_predictor:Gt},hn={box_encoding_predictor:qe,class_predictor:ln};return{conv_0:T,conv_1:C,conv_2:D,conv_3:_,conv_4:A,conv_5:B,conv_6:ne,conv_7:te,box_predictor_0:bt,box_predictor_1:ws,box_predictor_2:Cr,box_predictor_3:Rr,box_predictor_4:Ta,box_predictor_5:hn}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function ok(r){const l=[],{extractWeights:u,getRemainingWeights:p}=qn(r),{extractMobilenetV1Params:y,extractPredictionLayerParams:g}=hee(u,l),I=y(),S=g(),T=Li.tensor3d(u(5118*4),[1,5118,4]),C={extra_dim:T};if(l.push({paramPath:"output_layer/extra_dim"}),p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{params:{mobilenetv1:I,prediction_layer:S,output_layer:C},paramMappings:l}}function uee(r,l){const u=gs(r,l);function p(C,D,_){const A=u(`${C}/Conv2d_${D}_pointwise/weights`,4,`${_}/filters`),B=u(`${C}/Conv2d_${D}_pointwise/convolution_bn_offset`,1,`${_}/batch_norm_offset`);return{filters:A,batch_norm_offset:B}}function y(C){const D=`mobilenetv1/conv_${C}`,_=`MobilenetV1/Conv2d_${C}_depthwise`,A=`${D}/depthwise_conv`,B=`${D}/pointwise_conv`,ne=u(`${_}/depthwise_weights`,4,`${A}/filters`),te=u(`${_}/BatchNorm/gamma`,1,`${A}/batch_norm_scale`),P=u(`${_}/BatchNorm/beta`,1,`${A}/batch_norm_offset`),ge=u(`${_}/BatchNorm/moving_mean`,1,`${A}/batch_norm_mean`),ae=u(`${_}/BatchNorm/moving_variance`,1,`${A}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:P,batch_norm_mean:ge,batch_norm_variance:ae},pointwise_conv:p("MobilenetV1",C,B)}}function g(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:y(1),conv_2:y(2),conv_3:y(3),conv_4:y(4),conv_5:y(5),conv_6:y(6),conv_7:y(7),conv_8:y(8),conv_9:y(9),conv_10:y(10),conv_11:y(11),conv_12:y(12),conv_13:y(13)}}function I(C,D){const _=u(`${C}/weights`,4,`${D}/filters`),A=u(`${C}/biases`,1,`${D}/bias`);return{filters:_,bias:A}}function S(C){const D=I(`Prediction/BoxPredictor_${C}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${C}/box_encoding_predictor`),_=I(`Prediction/BoxPredictor_${C}/ClassPredictor`,`prediction_layer/box_predictor_${C}/class_predictor`);return{box_encoding_predictor:D,class_predictor:_}}function T(){return{conv_0:p("Prediction",0,"prediction_layer/conv_0"),conv_1:p("Prediction",1,"prediction_layer/conv_1"),conv_2:p("Prediction",2,"prediction_layer/conv_2"),conv_3:p("Prediction",3,"prediction_layer/conv_3"),conv_4:p("Prediction",4,"prediction_layer/conv_4"),conv_5:p("Prediction",5,"prediction_layer/conv_5"),conv_6:p("Prediction",6,"prediction_layer/conv_6"),conv_7:p("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:S(0),box_predictor_1:S(1),box_predictor_2:S(2),box_predictor_3:S(3),box_predictor_4:S(4),box_predictor_5:S(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:T}}function ak(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=uee(r,l),y=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!wr(y))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${y}`);const g={mobilenetv1:u(),prediction_layer:p(),output_layer:{extra_dim:y}};return Hn(r,l),{params:g,paramMappings:l}}const To=Ze(Qe());function Xs(r,l,u){return To.tidy(()=>{let p=To.conv2d(r,l.filters,u,"same");return p=To.add(p,l.batch_norm_offset),To.clipByValue(p,0,6)})}const Ar=Ze(Qe()),dee=.0010000000474974513;function pee(r,l,u){return Ar.tidy(()=>{let p=Ar.depthwiseConv2d(r,l.filters,u,"same");return p=Ar.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,dee),Ar.clipByValue(p,0,6)})}function mee(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function ck(r,l){return Ar.tidy(()=>{let u,p=Xs(r,l.conv_0,[2,2]);const y=[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(y.forEach((g,I)=>{const S=I+1,T=mee(S);p=pee(p,g.depthwise_conv,T),p=Xs(p,g.pointwise_conv,[1,1]),S===11&&(u=p)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:p,conv11:u}})}function lk(r,l,u,p,y){const g=r.shape[0],I=Math.min(u,g),S=l.map((D,_)=>({score:D,boxIndex:_})).filter(D=>D.score>y).sort((D,_)=>_.score-D.score),T=D=>D<=p?1:0,C=[];return S.forEach(D=>{if(C.length>=I)return;const _=D.score;for(let A=C.length-1;A>=0;--A){const B=fee(r,D.boxIndex,C[A]);if(B===0)continue;if(D.score*=T(B),D.score<=y)break}_===D.score&&C.push(D.boxIndex)}),C}function fee(r,l,u){const p=r.arraySync(),y=Math.min(p[l][0],p[l][2]),g=Math.min(p[l][1],p[l][3]),I=Math.max(p[l][0],p[l][2]),S=Math.max(p[l][1],p[l][3]),T=Math.min(p[u][0],p[u][2]),C=Math.min(p[u][1],p[u][3]),D=Math.max(p[u][0],p[u][2]),_=Math.max(p[u][1],p[u][3]),A=(I-y)*(S-g),B=(D-T)*(_-C);if(A<=0||B<=0)return 0;const ne=Math.max(y,T),te=Math.max(g,C),P=Math.min(I,D),ge=Math.min(S,_),ae=Math.max(P-ne,0)*Math.max(ge-te,0);return ae/(A+B-ae)}const ke=Ze(Qe());function gee(r){const l=ke.unstack(ke.transpose(r,[1,0])),u=[ke.sub(l[2],l[0]),ke.sub(l[3],l[1])],p=[ke.add(l[0],ke.div(u[0],ke.scalar(2))),ke.add(l[1],ke.div(u[1],ke.scalar(2)))];return{sizes:u,centers:p}}function yee(r,l){const{sizes:u,centers:p}=gee(r),y=ke.unstack(ke.transpose(l,[1,0])),g=ke.div(ke.mul(ke.exp(ke.div(y[2],ke.scalar(5))),u[0]),ke.scalar(2)),I=ke.add(ke.mul(ke.div(y[0],ke.scalar(10)),u[0]),p[0]),S=ke.div(ke.mul(ke.exp(ke.div(y[3],ke.scalar(5))),u[1]),ke.scalar(2)),T=ke.add(ke.mul(ke.div(y[1],ke.scalar(10)),u[1]),p[1]);return ke.transpose(ke.stack([ke.sub(I,g),ke.sub(T,S),ke.add(I,g),ke.add(T,S)]),[1,0])}function hk(r,l,u){return ke.tidy(()=>{const p=r.shape[0];let y=yee(ke.reshape(ke.tile(u.extra_dim,[p,1,1]),[-1,4]),ke.reshape(r,[-1,4]));y=ke.reshape(y,[p,y.shape[0]/p,4]);const g=ke.sigmoid(ke.slice(l,[0,0,1],[-1,-1,-1]));let I=ke.slice(g,[0,0,0],[-1,-1,1]);I=ke.reshape(I,[p,I.shape[1]]);const S=ke.unstack(y),T=ke.unstack(I);return{boxes:S,scores:T}})}const nd=Ze(Qe());function Ia(r,l){return nd.tidy(()=>{const u=r.shape[0],p=nd.reshape(Sa(r,l.box_encoding_predictor),[u,-1,1,4]),y=nd.reshape(Sa(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:y}})}const sd=Ze(Qe());function uk(r,l,u){return sd.tidy(()=>{const p=Xs(r,u.conv_0,[1,1]),y=Xs(p,u.conv_1,[2,2]),g=Xs(y,u.conv_2,[1,1]),I=Xs(g,u.conv_3,[2,2]),S=Xs(I,u.conv_4,[1,1]),T=Xs(S,u.conv_5,[2,2]),C=Xs(T,u.conv_6,[1,1]),D=Xs(C,u.conv_7,[2,2]),_=Ia(l,u.box_predictor_0),A=Ia(r,u.box_predictor_1),B=Ia(y,u.box_predictor_2),ne=Ia(I,u.box_predictor_3),te=Ia(T,u.box_predictor_4),P=Ia(D,u.box_predictor_5),ge=sd.concat([_.boxPredictionEncoding,A.boxPredictionEncoding,B.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,P.boxPredictionEncoding],1),ae=sd.concat([_.classPrediction,A.classPrediction,B.classPrediction,ne.classPrediction,te.classPrediction,P.classPrediction],1);return{boxPredictions:ge,classPredictions:ae}})}class Ys{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 Si=Ze(Qe());class sa extends En{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return Si.tidy(()=>{const u=Si.cast(r.toBatchTensor(512,!1),"float32"),p=Si.sub(Si.mul(u,Si.scalar(.007843137718737125)),Si.scalar(1)),y=ck(p,l.mobilenetv1),{boxPredictions:g,classPredictions:I}=uk(y.out,y.conv11,l.prediction_layer);return hk(g,I,l.output_layer)})}async forward(r){return this.forwardInput(await Ot(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new Ys(l),y=await Ot(r),{boxes:g,scores:I}=this.forwardInput(y),S=g[0],T=I[0];for(let ae=1;ae<g.length;ae++)g[ae].dispose(),I[ae].dispose();const C=Array.from(await T.data()),D=.5,_=lk(S,C,u,D,p),A=y.getReshapedInputDimensions(0),B=y.inputSize,ne=B/A.width,te=B/A.height,P=S.arraySync(),ge=_.map(ae=>{const[Le,ve]=[Math.max(0,P[ae][0]),Math.min(1,P[ae][2])].map(pt=>pt*te),[Ve,at]=[Math.max(0,P[ae][1]),Math.min(1,P[ae][3])].map(pt=>pt*ne);return new Vt(C[ae],new Bc(Ve,Le,at-Ve,ve-Le),{height:y.getInputHeight(0),width:y.getInputWidth(0)})});return S.dispose(),T.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return ak(r)}extractParams(r){return ok(r)}}function rI(r){const l=new sa;return l.extractWeights(r),l}function m2(r){return rI(r)}class f2 extends sa{}const dk=.4,pk=[new Ke(.738768,.874946),new Ke(2.42204,2.65704),new Ke(4.30971,7.04493),new Ke(10.246,4.59428),new Ke(12.6868,11.8741)],mk=[new Ke(1.603231,2.094468),new Ke(6.041143,7.080126),new Ke(2.882459,3.518061),new Ke(4.266906,5.178857),new Ke(9.041765,10.66308)],fk=[117.001,114.697,97.404],gk="tiny_yolov2_model",yk="tiny_yolov2_separable_conv_model";const dy=r=>typeof r=="number";function tf(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(!dy(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=>dy(l.x)&&dy(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(dy)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Js=Ze(Qe());function ll(r){return Js.tidy(()=>{const l=Js.mul(r,Js.scalar(.10000000149011612));return Js.add(Js.relu(Js.sub(r,l)),l)})}const Zs=Ze(Qe());function vr(r,l){return Zs.tidy(()=>{let u=Zs.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=Zs.conv2d(u,l.conv.filters,[1,1],"valid"),u=Zs.sub(u,l.bn.sub),u=Zs.mul(u,l.bn.truediv),u=Zs.add(u,l.conv.bias),ll(u)})}const Ao=Ze(Qe());function Nr(r,l){return Ao.tidy(()=>{let u=Ao.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=Ao.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=Ao.add(u,l.bias),ll(u)})}const Nx=Ze(Qe());function bee(r,l){const u=sl(r,l);function p(I,S){const T=Nx.tensor1d(r(I)),C=Nx.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:T,truediv:C}}function y(I,S,T){const C=u(I,S,3,`${T}/conv`),D=p(S,`${T}/bn`);return{conv:C,bn:D}}const g=il(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:y,extractSeparableConvParams:g}}function bk(r,l,u,p){const{extractWeights:y,getRemainingWeights:g}=qn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:T,extractSeparableConvParams:C}=bee(y,I);let D;if(l.withSeparableConvs){const[_,A,B,ne,te,P,ge,ae,Le]=p,ve=l.isFirstLayerConv2d?S(_,A,3,"conv0"):C(_,A,"conv0"),Ve=C(A,B,"conv1"),at=C(B,ne,"conv2"),pt=C(ne,te,"conv3"),$t=C(te,P,"conv4"),Gt=C(P,ge,"conv5"),qe=ae?C(ge,ae,"conv6"):void 0,ln=Le?C(ae,Le,"conv7"):void 0,bt=S(Le||ae||ge,5*u,1,"conv8");D={conv0:ve,conv1:Ve,conv2:at,conv3:pt,conv4:$t,conv5:Gt,conv6:qe,conv7:ln,conv8:bt}}else{const[_,A,B,ne,te,P,ge,ae,Le]=p,ve=T(_,A,"conv0"),Ve=T(A,B,"conv1"),at=T(B,ne,"conv2"),pt=T(ne,te,"conv3"),$t=T(te,P,"conv4"),Gt=T(P,ge,"conv5"),qe=T(ge,ae,"conv6"),ln=T(ae,Le,"conv7"),bt=S(Le,5*u,1,"conv8");D={conv0:ve,conv1:Ve,conv2:at,conv3:pt,conv4:$t,conv5:Gt,conv6:qe,conv7:ln,conv8:bt}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:D,paramMappings:I}}function wee(r,l){const u=gs(r,l);function p(S){const T=u(`${S}/sub`,1),C=u(`${S}/truediv`,1);return{sub:T,truediv:C}}function y(S){const T=u(`${S}/filters`,4),C=u(`${S}/bias`,1);return{filters:T,bias:C}}function g(S){const T=y(`${S}/conv`),C=p(`${S}/bn`);return{conv:T,bn:C}}const I=rl(u);return{extractConvParams:y,extractConvWithBatchNormParams:g,extractSeparableConvParams:I}}function wk(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:y,extractSeparableConvParams:g}=wee(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:S>7?g("conv6"):void 0,conv7:S>8?g("conv7"):void 0,conv8:p("conv8")}}else I={conv0:y("conv0"),conv1:y("conv1"),conv2:y("conv2"),conv3:y("conv3"),conv4:y("conv4"),conv5:y("conv5"),conv6:y("conv6"),conv7:y("conv7"),conv8:p("conv8")};return Hn(r,u),{params:I,paramMappings:u}}var nf;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(nf||(nf={}));class Wi{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 Mt=Ze(Qe());class hl extends En{constructor(r){super("TinyYolov2");tf(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 u=vr(r,l.conv0);return u=Mt.maxPool(u,[2,2],[2,2],"same"),u=vr(u,l.conv1),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=vr(u,l.conv2),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=vr(u,l.conv3),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=vr(u,l.conv4),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=vr(u,l.conv5),u=Mt.maxPool(u,[2,2],[1,1],"same"),u=vr(u,l.conv6),u=vr(u,l.conv7),Sa(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?ll(Sa(r,l.conv0,"valid",!1)):Nr(r,l.conv0);return u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Nr(u,l.conv1),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Nr(u,l.conv2),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Nr(u,l.conv3),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Nr(u,l.conv4),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Nr(u,l.conv5),u=Mt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?Nr(u,l.conv6):u,u=l.conv7?Nr(u,l.conv7):u,Sa(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return Mt.tidy(()=>{let p=Mt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?Hs(p,this.config.meanRgb):p,p=p.div(Mt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Ot(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new Wi(l),y=await Ot(r),g=await this.forwardInput(y,u),I=Mt.tidy(()=>Mt.unstack(g)[0].expandDims()),S={width:y.getInputWidth(0),height:y.getInputHeight(0)},T=await this.extractBoxes(I,y.getReshapedInputDimensions(0),p);g.dispose(),I.dispose();const C=T.map(te=>te.box),D=T.map(te=>te.score),_=T.map(te=>te.classScore),A=T.map(te=>this.config.classes[te.label]),B=of(C.map(te=>te.rescale(u)),D,this.config.iouThreshold,!0),ne=B.map(te=>new ca(D[te],_[te],A[te],C[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return wk(r,this.config)}extractParams(r){const l=this.config.filterSizes||hl.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return bk(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:y}=l,g=Math.max(p,y),I=g/p,S=g/y,T=r.shape[1],C=this.config.anchors.length,[D,_,A]=Mt.tidy(()=>{const P=r.reshape([T,T,C,this.boxEncodingSize]),ge=P.slice([0,0,0,0],[T,T,C,4]),ae=P.slice([0,0,0,4],[T,T,C,1]),Le=this.withClassScores?Mt.softmax(P.slice([0,0,0,5],[T,T,C,this.config.classes.length]),3):Mt.scalar(0);return[ge,ae,Le]}),B=[],ne=await _.array(),te=await D.array();for(let P=0;P<T;P++)for(let ge=0;ge<T;ge++)for(let ae=0;ae<C;ae++){const Le=Fc(ne[P][ge][ae][0]);if(!u||Le>u){const ve=(ge+Fc(te[P][ge][ae][0]))/T*I,Ve=(P+Fc(te[P][ge][ae][1]))/T*S,at=Math.exp(te[P][ge][ae][2])*this.config.anchors[ae].x/T*I,pt=Math.exp(te[P][ge][ae][3])*this.config.anchors[ae].y/T*S,$t=ve-at/2,Gt=Ve-pt/2,qe={row:P,col:ge,anchor:ae},{classScore:ln,label:bt}=this.withClassScores?await this.extractPredictedClass(A,qe):{classScore:1,label:0};B.push({box:new Pc($t,Gt,$t+at,Gt+pt),score:Le,classScore:Le*ln,label:bt,...qe})}}return D.dispose(),_.dispose(),A.dispose(),B}async extractPredictedClass(r,l){const{row:u,col:p,anchor:y}=l,g=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>g[u][p][y][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}hl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Dc extends hl{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:dk,classes:["face"]},r?{anchors:mk,meanRgb:fk}:{anchors:pk,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Vt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?yk:gk}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function d2(r,l=!0){const u=new Dc(l);return u.extractWeights(r),u}class sf extends Wi{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class qs{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const Cx=Ze(Qe());async function xa(r,l,u,p,y=({alignedRect:g})=>g){const g=r.map(T=>oo(T)?y(T):T.detection),I=p||(l instanceof Cx.Tensor?await oa(l,g):await ra(l,g)),S=await u(I);return I.forEach(T=>T instanceof Cx.Tensor&&T.dispose()),S}async function ul(r,l,u,p,y){return xa([r],l,async g=>u(g[0]),p,y)}const Lk=.4,Sk=[new Ke(1.603231,2.094468),new Ke(6.041143,7.080126),new Ke(2.882459,3.518061),new Ke(4.266906,5.178857),new Ke(9.041765,10.66308)],Ik=[117.001,114.697,97.404];class kc extends hl{constructor(){const r={withSeparableConvs:!0,iouThreshold:Lk,classes:["face"],anchors:Sk,meanRgb:Ik,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Vt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const ft={ssdMobilenetv1:new sa,tinyFaceDetector:new kc,tinyYolov2:new Dc,faceLandmark68Net:new Wc,faceLandmark68TinyNet:new gf,faceRecognitionNet:new _c,faceExpressionNet:new bf,ageGenderNet:new vf},aI=(r,l)=>ft.ssdMobilenetv1.locateFaces(r,l),b2=(r,l)=>ft.tinyFaceDetector.locateFaces(r,l),w2=(r,l)=>ft.tinyYolov2.locateFaces(r,l),cI=r=>ft.faceLandmark68Net.detectLandmarks(r),L2=r=>ft.faceLandmark68TinyNet.detectLandmarks(r),S2=r=>ft.faceRecognitionNet.computeFaceDescriptor(r),I2=r=>ft.faceExpressionNet.predictExpressions(r),x2=r=>ft.ageGenderNet.predictAgeAndGender(r),lI=r=>ft.ssdMobilenetv1.load(r),T2=r=>ft.tinyFaceDetector.load(r),A2=r=>ft.tinyYolov2.load(r),v2=r=>ft.faceLandmark68Net.load(r),N2=r=>ft.faceLandmark68TinyNet.load(r),C2=r=>ft.faceRecognitionNet.load(r),R2=r=>ft.faceExpressionNet.load(r),O2=r=>ft.ageGenderNet.load(r),E2=lI,D2=aI,k2=cI;class xk extends qs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class od extends xk{async run(){const r=await this.parentTask,l=await xa(r,this.input,async u=>await Promise.all(u.map(p=>ft.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>Ou(u,l[p]))}withAgeAndGender(){return new id(this,this.input)}}class ad extends xk{async run(){const r=await this.parentTask;if(!r)return;const l=await ul(r,this.input,u=>ft.faceExpressionNet.predictExpressions(u),this.extractedFaces);return Ou(r,l)}withAgeAndGender(){return new rd(this,this.input)}}class ml extends od{withAgeAndGender(){return new dl(this,this.input)}withFaceDescriptors(){return new io(this,this.input)}}class fl extends ad{withAgeAndGender(){return new pl(this,this.input)}withFaceDescriptor(){return new ro(this,this.input)}}class Tk extends qs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class id extends Tk{async run(){const r=await this.parentTask,l=await xa(r,this.input,async u=>await Promise.all(u.map(p=>ft.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:y,gender:g,genderProbability:I}=l[p];return Du(Ru(u,g,I),y)})}withFaceExpressions(){return new od(this,this.input)}}class rd extends Tk{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await ul(r,this.input,y=>ft.ageGenderNet.predictAgeAndGender(y),this.extractedFaces);return Du(Ru(r,u,p),l)}withFaceExpressions(){return new ad(this,this.input)}}class dl extends id{withFaceExpressions(){return new ml(this,this.input)}withFaceDescriptors(){return new io(this,this.input)}}class pl extends rd{withFaceExpressions(){return new fl(this,this.input)}withFaceDescriptor(){return new ro(this,this.input)}}class mf extends qs{constructor(r,l){super();this.parentTask=r;this.input=l}}class io extends mf{async run(){const r=await this.parentTask,l=await xa(r,this.input,u=>Promise.all(u.map(p=>ft.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>Eu(r[p],u))}withFaceExpressions(){return new ml(this,this.input)}withAgeAndGender(){return new dl(this,this.input)}}class ro extends mf{async run(){const r=await this.parentTask;if(!r)return;const l=await ul(r,this.input,u=>ft.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return Eu(r,l)}withFaceExpressions(){return new fl(this,this.input)}withAgeAndGender(){return new pl(this,this.input)}}const cd=Ze(Qe());class uf extends qs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?ft.faceLandmark68TinyNet:ft.faceLandmark68Net}}class df extends uf{async run(){const r=await this.parentTask,l=r.map(y=>y.detection),u=this.input instanceof cd.Tensor?await oa(this.input,l):await ra(this.input,l),p=await Promise.all(u.map(y=>this.landmarkNet.detectLandmarks(y)));return u.forEach(y=>y instanceof cd.Tensor&&y.dispose()),r.map((y,g)=>ia(y,p[g]))}withFaceExpressions(){return new ml(this,this.input)}withAgeAndGender(){return new dl(this,this.input)}withFaceDescriptors(){return new io(this,this.input)}}class pf extends uf{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof cd.Tensor?await oa(this.input,[l]):await ra(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(y=>y instanceof cd.Tensor&&y.dispose()),ia(r,p)}withFaceExpressions(){return new fl(this,this.input)}withAgeAndGender(){return new pl(this,this.input)}withFaceDescriptor(){return new ro(this,this.input)}}class lf extends qs{constructor(r,l=new Ys){super();this.input=r;this.options=l}}class Cu extends lf{async run(){const{input:r,options:l}=this,u=l instanceof sf?p=>ft.tinyFaceDetector.locateFaces(p,l):l instanceof Ys?p=>ft.ssdMobilenetv1.locateFaces(p,l):l instanceof Wi?p=>ft.tinyYolov2.locateFaces(p,l):null;if(!u)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return u(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(u=>ao({},u)))})}withFaceLandmarks(r=!1){return new df(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new od(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new id(this.runAndExtendWithFaceDetections(),this.input)}}class hf extends lf{async run(){const r=await new Cu(this.input,this.options);let l=r[0];return r.forEach(u=>{u.score>l.score&&(l=u)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?ao({},l):void 0)})}withFaceLandmarks(r=!1){return new pf(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new ad(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new rd(this.runAndExtendWithFaceDetection(),this.input)}}function F2(r,l=new Ys){return new hf(r,l)}function Nu(r,l=new Ys){return new Cu(r,l)}async function hI(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Nu(r,new Ys(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function _2(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Nu(r,new Wi(l)).withFaceLandmarks().withFaceDescriptors()}const W2=hI;function wf(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const u=Array.from(r),p=Array.from(l);return Math.sqrt(u.map((y,g)=>y-p[g]).reduce((y,g)=>y+Math.pow(g,2),0))}class uI{constructor(r,l=.6){this._distanceThreshold=l;const u=Array.isArray(r)?r:[r];if(!u.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let p=1;const y=()=>`person ${p++}`;this._labeledDescriptors=u.map(g=>{if(g instanceof po)return g;if(g instanceof Float32Array)return new po(y(),[g]);if(g.descriptor&&g.descriptor instanceof Float32Array)return new po(y(),[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(u=>wf(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new _u(u,this.computeMeanDistance(r,l))).reduce((l,u)=>l.distance<u.distance?l:u)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new _u("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>po.fromJSON(u));return new uI(l,r.distanceThreshold)}}function p2(r){const l=new kc;return l.extractWeights(r),l}function oI(r,l){const{width:u,height:p}=new rs(l.width,l.height);if(u<=0||p<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:p})}`);if(Array.isArray(r))return r.map(y=>oI(y,{width:u,height:p}));if(oo(r)){const y=r.detection.forSize(u,p),g=r.unshiftedLandmarks.forSize(y.box.width,y.box.height);return ia(ao(r,y),g)}return mi(r)?ao(r,r.detection.forSize(u,p)):r instanceof Rs||r instanceof Vt?r.forSize(u,p):r}var h2="0.8.5";return H2();})();
/**
* @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.js.map
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