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Buffer shape=${this.shape}`;throw new Error(o)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return qt().makeTensor(this.values,this.shape,this.dtype)}}let qt=null,Fr=null,Ew=null;function Cd(e){qt=e}function Ed(e){Fr=e}function Od(e){Ew=e}class ee{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Q(e),this.strides=Gt(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Fr.buffer(this.shape,this.dtype,e)}bufferSync(){return Fr.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Mn(this.shape,e)}arraySync(){return Mn(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=qt().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>_a(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=qt().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>_a(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 qt().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;qt().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Fr.print(this,e)}clone(){return this.throwIfDisposed(),Fr.clone(this)}toString(e=!1){const t=this.dataSync();return Rd(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Fr.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),qt().makeVariable(this,e,t,n)}}Object.defineProperty(ee,Symbol.hasInstance,{value:e=>!!e&&e.dataId!=null&&e.shape!=null&&e.dtype!=null});class Ht extends ee{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Te(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);qt().disposeTensor(this),this.dataId=e.dataId,qt().incRef(this,null)}dispose(){qt().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Ht,Symbol.hasInstance,{value:e=>e instanceof ee&&e.assign!=null&&e.assign instanceof Function});var Wp;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Wp||(Wp={}));var Bp;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Bp||(Bp={}));var $p;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})($p||($p={}));var jp;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(jp||(jp={}));var Gp;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Gp||(Gp={}));const Ow={float32:jp,int32:Bp,bool:$p,complex64:Gp};function Mr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Ow[e][t]}function kw(e){return Mr(e,"int32")}const kd={};Re(kd,{assertTypesMatch:()=>Pp,getTensorsInContainer:()=>ls,isTensorInList:()=>Dw,makeTypesMatch:()=>V});function V(e,t){if(e.dtype===t.dtype)return[e,t];const n=Mr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Pp(e,t){f(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Dw(e,t){return t.some(n=>n.id===e.id)}function ls(e){const t=[],n=new Set();return Dd(e,t,n),t}function Dd(e,t,n){if(e==null)return;if(e instanceof ee){t.push(e);return}if(!Fw(e))return;const r=e;for(const o in r){const i=r[o];n.has(i)||(n.add(i),Dd(i,t,n))}}function Fw(e){return Array.isArray(e)||typeof e=="object"}class Fd{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap(),this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class hs{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Fd()}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],r=await this.initializeBackend(n).success;if(r){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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),r=n?await t:t;if(!r)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Id(this.backendInstance),!0}setupRegisteredKernels(){const e=Na(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Na(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(Promise.resolve(n)===n){const r=++this.pendingBackendInitId,o=n.then(i=>r<this.pendingBackendInitId?!1:(this.registry[e]=i,this.pendingBackendInit=null,!0)).catch(i=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(i.stack||i.message)),!1));return this.pendingBackendInit=o,{success:o,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:r,asyncInit:o}=this.initializeBackend(n);if(o||r)return{name:n,asyncInit:o}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,o=this.readSync(t);r.disposeData(t),n.backend=e,e.move(t,o,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 r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{const r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return hs.nextTensorId++}nextVariableId(){return hs.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},r=i=>({x:()=>{const a="float32",s={x:i},c={dtype:a};return g.runKernelFunc(p=>p.cast(i,a),s,null,Dn,c)}}),o=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,o,{}),t}runKernel(e,t,n,r,o){const i=null,a=null;return this.runKernelFunc(i,t,a,e,n,r,o)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let o=0;n.forEach(s=>{o+=s.dtype==="complex64"?3:1});const i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=r-t-o-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,r,o,i,a){let s,c=[];const p=this.isTapeOn();r==null&&(r=this.state.activeScope!=null?this.state.activeScope.name:"");const l=this.state.numBytes,h=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let d;const b=rs(r,this.backendName);let x;if(b!=null)d=()=>{const L=this.backend.numDataIds();x=b.kernelFunc({inputs:t,attrs:o,backend:this.backend});const S=Array.isArray(x)?x:[x];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(r,L,S);const I=S.map(({dataId:N,shape:A,dtype:E})=>this.makeTensorFromDataId(N,A,E));if(p){let N=this.getTensorsForGradient(r,t,I);if(N==null){a==null&&(a=[]);const A=I.filter((E,M)=>a[M]);N=(i||[]).slice().concat(A)}c=this.saveTensorsForBackwardMode(N)}return I};else{const L=S=>{if(!p)return;c=S.map(I=>this.keep(this.clone(I)))};d=()=>{const S=this.backend.numDataIds();x=this.tidy(()=>e(this.backend,L));const I=Array.isArray(x)?x:[x];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(r,S,I),I}}let w;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?s=d():(w=this.profiler.profileKernel(r,t,()=>d()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(w),s=w.outputs)}),p&&this.addTapeNode(r,t,s,n,c,o),this.state.profiling&&this.state.activeProfile.kernels.push({name:r,bytesAdded:this.state.numBytes-l,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-h,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(L=>t[L]!=null?t[L].shape:null),outputShapes:s.map(L=>L.shape),kernelTimeMs:w.timeMs,extraInfo:w.extraInfo}),Array.isArray(x)?s:s[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const r=Aa(e);if(r!=null){const o=r.inputsToSave||[],i=r.outputsToSave||[];let a;r.saveAllInputs?(f(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(c=>t[c])):a=o.map(c=>t[c]);const s=n.filter((c,p)=>i[p]);return a.concat(s)}return null}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let o=e;n==="string"&&Rt(e[0])&&(o=e.map(s=>Mp(s)));const i=r.write(o,t,n),a=new ee(t,n,i,this.nextTensorId());if(this.incRef(a,r),n==="string"){const s=this.state.tensorInfo.get(i),c=Fp(o);this.state.numBytes+=c-s.bytes,s.bytes=c}return a}makeTensorFromDataId(e,t,n,r){n=n||"float32";const o=new ee(t,n,e,this.nextTensorId());return this.incRef(o,r),o}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));const o=new Ht(e,t,n,this.nextTensorId());if(this.state.registeredVariables[o.name]!=null)throw new Error(`Variable with name ${o.name} was already registered`);return this.state.registeredVariables[o.name]=o,this.incRef(o,this.backend),o}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 r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*Dp(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r,refCount:0}),this.state.numBytes+=r}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Ht||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(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,o,i){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:o},s=Aa(e);s!=null&&(r=s.gradFunc),r!=null&&(a.gradient=c=>(c=c.map((p,l)=>{if(p==null){const h=n[l],d=Pt(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return p}),r(c.length>1?c:c[0],o,i))),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=ls(e),n=new Set(t.map(o=>o.id));for(let o=0;o<this.state.activeScope.track.length;o++){const i=this.state.activeScope.track[o];!i.kept&&!n.has(i.id)&&i.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(o=>{!o.kept&&o.scopeId===r.id&&this.track(o)})}gradients(e,t,n,r=!1){if(f(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 o=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));f(o instanceof ee,()=>"The result y returned by f() must be a tensor.");const i=Td(this.state.activeTape,t,o);if(!r&&i.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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shape: ${e}, sliceDim: ${r}, and batchDim: ${o}.`;if(n.rank<o)throw new Error(i+` update.rank < ${o}. `);if(e.length<r+(n.rank-o))throw new Error(i+` Output shape length < ${r+(n.rank-o)}`);if(n.rank!==o+e.length-r)throw new Error(i+` update.rank != ${o+e.length-r}`);for(let a=0;a<o;++a)if(n.shape[a]!==t.shape[a])throw new Error(i+` updates.shape[${a}] (${n.shape[a]}) != indices.shape[${a}] (${t.shape[a]}).`);for(let a=0;a<n.rank-o;++a)if(n.shape[a+o]!==e[a+r])throw new Error(i+` updates.shape[${a+o}] (${n.shape[a+o]}) != shape[${a+o}] (${e[a+o]})`)}function Ba(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got 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o=e.rank>0?e.shape[0]:1,i=e.rank>1?e.shape[1]:1;if(n.length!==i)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${i}.`);const a=t.size;if(!(t.rank===0||t.rank===1&&a===o))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${o}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function Kv(e,t,n,r=0){const o=u(e,"sparseIndices","sparseToDense","int32"),i=u(t,"sparseValues","sparseToDense"),a=u(r,"defaultValue","sparseToDense",i.dtype);gm(o,i,n,a);const s={sparseIndices:o,sparseValues:i,defaultValue:a},c={outputShape:n};return g.runKernelFunc(p=>p.sparseToDense(o,i,n,a),s,null,Sp,c)}const bm=m({sparseToDense_:Kv});function Jv(e,t){const n=u(t,"indices","gatherND","int32"),r=u(e,"x","gatherND"),o=a=>a.gatherND(r,n),i={params:r,indices:n};return g.runKernelFunc(o,i,null,Zc)}const wm=m({gatherND_:Jv});function xm(e,t){if(t==null)return e.shape.slice();if(Te(e.shape,t))return t;if(e.shape.length===t.length){const n=[];for(let r=0;r<e.shape.length;r++)t[r]==null&&e.shape[r]!=null?n.push(e.shape[r]):n.push(t[r]);return n}return t}function Xv(e,t,n,r){const o=u(e,"x","dropout");if(f(o.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${o.dtype} tensor instead.`),f(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof ee?o.clone():o;const i=xm(o,n),a=1-t,s=F(Pn(R(Ns(i,0,1,"float32",r),a)),a);return v(o,s)}const ym=m({dropout_:Xv});function $a(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function qr(e,t,n){const r=1-e%2,o=new Float32Array(e);for(let i=0;i<e;++i){const a=2*Math.PI*i/(e+r-1);o[i]=t-n*Math.cos(a)}return ce(o,"float32")}async function Zv(e,t,n=1){const r=u(e,"predictions","inTopK"),o=u(t,"targets","inTopK");f(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got 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Error(`Cannot compute gradient for fused activation ${n}.`)}function nr(e,t){let n=t;const r=ae(e.shape,t.shape);return r.length>0&&(n=U(n,r)),y(n,e.shape)}function rr(e,t,n){if(t==="linear")return e;if(t==="relu")return ie(e);if(t==="elu")return bs(e);if(t==="relu6")return Rs(e);if(t==="prelu")return Ts(e,n);throw new Error(`Unknown fused activation ${t}.`)}const or=(e,t)=>{const n=e>0;return!n||t==="linear"};function eS({x:e,filter:t,strides:n,pad:r,dataFormat:o="NHWC",dilations:i=[1,1],dimRoundingMode:a,bias:s,activation:c="linear",preluActivationWeights:p}){if(c=c||"linear",or(g.state.gradientDepth,c)===!1){let E=xe(e,t,n,r,o,i,a);return s!=null&&(E=R(E,s)),rr(E,c,p)}const l=u(e,"x","conv2d"),h=u(t,"filter","conv2d");let d=l,b=!1;l.rank===3&&(b=!0,d=y(l,[1,l.shape[0],l.shape[1],l.shape[2]])),f(d.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`),f(h.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank 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Got strides ${n} and dilations '${i}'`),a!=null&&f(X(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${a} but got pad ${r}.`);const x=je(d.shape,h.shape,n,i,r,a,!0);let w;s!=null&&(w=u(s,"bias","fused conv2d"),[w]=V(w,l),q(x.outShape,w.shape));let L;p!=null&&(L=u(p,"prelu weights","fused depthwiseConv2d"));const S=(E,M)=>{f(ct(i),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. 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M=(j,te)=>{const[re,he,ye,Me]=te,Tt=tr(y(j,ye.shape),ye,i);let ln,wr;if(!n&&!r?(ln=Y(Tt,he,!1,!0),wr=Y(re,Tt,!0,!1)):!n&&r?(ln=Y(Tt,he,!1,!1),wr=Y(Tt,re,!0,!1)):n&&!r?(ln=Y(he,Tt,!1,!0),wr=Y(re,Tt,!1,!1)):(ln=Y(he,Tt,!0,!0),wr=Y(Tt,re,!0,!0)),o!=null){const Yb=nr(Me,Tt);return[ln,wr,Yb]}else return[ln,wr]},D=j=>{const te=j.fusedBatchMatMul({a:I,b:N,transposeA:n,transposeB:r,bias:A,activation:i,preluActivationWeights:E});return te},$={a:I,b:N,bias:A,preluActivationWeights:E},B={transposeA:n,transposeB:r,activation:i};if(o==null){const j=Ke((te,re,he)=>{const ye=g.runKernelFunc(D,$,null,Sa,B);return he([te,re,ye]),{value:y(ye,S),gradFunc:M}});return j(I,N)}else{const j=Ke((te,re,he,ye)=>{const Me=g.runKernelFunc(D,$,null,Sa,B);return ye([te,re,Me,he]),{value:y(Me,S),gradFunc:M}});return j(I,N,A)}}const aS=m({fusedMatMul_:sS});const Mh={};Re(Mh,{conv2d:()=>tS,depthwiseConv2d:()=>iS,matMul:()=>aS});function cS(e){return qr(e,.54,.46)}const vm=m({hammingWindow_:cS});function pS(e){return qr(e,.5,.5)}const Pa=m({hannWindow_:pS});function lS(e,t,n,r=!1,o=0){let i=0;const a=[];for(;i+t<=e.size;)a.push(G(e,i,t)),i+=n;if(r)for(;i<e.size;){const s=i+t-e.size,c=Z([G(e,i,t-s),Ge([s],o)]);a.push(c),i+=n}return a.length===0?mt([],[0,t]):y(Z(a),[a.length,t])}const qa=m({frame_:lS});function hS(e,t,n,r,o=Pa){r==null&&(r=$a(t));const i=qa(e,t,n),a=v(i,o(t)),s=[];for(let c=0;c<i.shape[0];c++)s.push(Jn(G(a,[c,0],[1,t]),r));return Z(s)}const Sm=m({stft_:hS});function uS(e,t,n,r,o,i){const a=u(e,"image","cropAndResize"),s=u(t,"boxes","cropAndResize","float32"),c=u(n,"boxInd","cropAndResize","int32");o=o||"bilinear",i=i||0;const p=s.shape[0];f(a.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`),f(s.rank===2&&s.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${p},4] but had shape ${s.shape}.`),f(c.rank===1&&c.shape[0]===p,()=>`Error in cropAndResize: boxInd must be have size [${p}] but had shape ${s.shape}.`),f(r.length===2,()=>`Error 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St(e,t,n,r,o,i){r==null&&(r=.5),o==null&&(o=Number.NEGATIVE_INFINITY),i==null&&(i=0);const a=e.shape[0];return n=Math.min(n,a),f(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),f(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),f(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),f(t.rank===1,()=>"scores must be a 1D tensor"),f(t.shape[0]===a,()=>`scores has incompatible shape with boxes. 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Manifest JSON has weights with names: ${s.join(", ")}.`)}const c=o.reduce((b,x,w)=>(x&&b.push(w),b),[]),p=[];c.forEach(b=>{t[b].paths.forEach(x=>{const w=n+(n.endsWith("/")?"":"/")+x;p.push(w)})});const l=await e(p),h={};let d=0;return c.forEach(b=>{const x=t[b].paths.length;let w=0;for(let A=0;A<x;A++)w+=l[d+A].byteLength;const L=new ArrayBuffer(w),S=new Uint8Array(L);let I=0;for(let A=0;A<x;A++){const E=new Uint8Array(l[d+A]);S.set(E,I),I+=E.byteLength}const N=i[b];N.forEach(A=>{const E=L.slice(A.groupOffset,A.groupOffset+A.sizeBytes),M=Bh(E,[A.manifestEntry]);for(const D in M)h[D]=M[D]}),d+=x}),h}}const aI="application/octet-stream",cI="application/json";class Ph{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,t.fetchFunc!=null?(f(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=pe().platform.fetch,f(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&f(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData();const n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata,weightsManifest:n};t.body.append("model.json",new Blob([JSON.stringify(r)],{type:cI}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:aI}),"model.weights.bin");const o=await this.fetch(this.path,t);if(o.ok)return{modelArtifactsInfo:Us(e),responses:[o]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${o.status}.`)}async load(){const e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(l){let h=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?h+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":h+=" Please make sure the server is serving valid JSON for this request.",new Error(h)}const n=t.modelTopology,r=t.weightsManifest,o=t.generatedBy,i=t.convertedBy,a=t.format,s=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let c,p;if(r!=null){const l=await this.loadWeights(r);[c,p]=l}return{modelTopology:n,weightSpecs:c,weightData:p,userDefinedMetadata:s,generatedBy:o,convertedBy:i,format:a}}async loadWeights(e){const t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=pI(t),o=this.weightPathPrefix||n,i=[];for(const c of e)i.push(...c.weights);const a=[];e.forEach(c=>{c.paths.forEach(p=>{a.push(o+p+r)})});const s=await Gh(a,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[i,Ms(s)]}}Ph.URL_SCHEME_REGEX=/^https?:\/\//;function pI(e){const t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),o=n>t?e.substring(n):"";return[r+"/",o]}function qh(e){return e.match(Ph.URL_SCHEME_REGEX)!=null}const cf=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>qh(r)):n=qh(e),n)return Hh(e,t)}return null};Ne.registerSaveRouter(cf);Ne.registerLoadRouter(cf);function Hh(e,t){return new Ph(e,t)}function lI(e,t){return Hh(e,t)}class zh{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}}class hI{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}}function uI(e,t,n,r){if(arguments.length===1){const o=e.modelTopology!=null||e.weightSpecs!=null;return o?new zh(e):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new zh({modelTopology:e}))}else return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new zh({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r})}function dI(e){return new hI(e)}const Vr={};Re(Vr,{browserFiles:()=>iI,browserHTTPRequest:()=>lI,concatenateArrayBuffers:()=>Ms,copyModel:()=>rf,decodeWeights:()=>Bh,encodeWeights:()=>PS,fromMemory:()=>uI,getLoadHandlers:()=>ZS,getModelArtifactsInfoForJSON:()=>Us,getSaveHandlers:()=>XS,http:()=>Hh,isHTTPScheme:()=>qh,listModels:()=>tf,loadWeights:()=>sI,moveModel:()=>of,registerLoadRouter:()=>JS,registerSaveRouter:()=>KS,removeModel:()=>nf,weightsLoaderFactory:()=>af,withSaveHandler:()=>dI});function mI(e,t,n){const r=u(e,"labels","confusionMatrix"),o=u(t,"predictions","confusionMatrix");f(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),f(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),f(o.rank===1,()=>`Expected the rank of predictions to be 1, but got ${o.rank}`),f(r.shape[0]===o.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${o.shape[0]}. Labels and predictions should have the same number of elements.`),f(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);const i=jr(C(r,"int32"),n),a=jr(C(o,"int32"),n),s=K(i);return C(Y(s,a),"int32")}const fI=m({confusionMatrix_:mI});const pf={};Re(pf,{confusionMatrix:()=>fI});const Kr={};Re(Kr,{fromPixels:()=>wI,toPixels:()=>bI});let Jr;function gI(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,o=!1,i=!1,a=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)o=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)i=!0;else if(e.getContext!=null)a=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(o){const b=2;if(o&&e.readyState<b)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}const s=rs(va,g.backendName);if(s!=null){const b={pixels:e},x={numChannels:t};return g.runKernel(va,b,x)}const[c,p]=o?[e.videoWidth,e.videoHeight]:[e.width,e.height];let l;a?l=e.getContext("2d").getImageData(0,0,c,p).data:r||n?l=e.data:(i||o)&&(Jr==null&&(Jr=document.createElement("canvas").getContext("2d")),Jr.canvas.width=c,Jr.canvas.height=p,Jr.drawImage(e,0,0,c,p),l=Jr.getImageData(0,0,c,p).data);let h;if(t===4)h=new Int32Array(l);else{const b=c*p;h=new Int32Array(b*t);for(let x=0;x<b;x++)for(let w=0;w<t;++w)h[x*t+w]=l[x*4+w]}const d=[p,c,t];return er(h,d,"int32")}async function bI(e,t){let n=u(e,"img","toPixels");if(!(e instanceof ee)){const L=n;n=C(L,"int32"),L.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);const[r,o]=n.shape.slice(0,2),i=n.rank===2?1:n.shape[2];if(i>4||i===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${i}`);const a=await n.data(),s=In(n),c=dt(n),p=await Promise.all([s.data(),c.data()]),l=p[0],h=p[1],d=l[0],b=h[0];if(s.dispose(),c.dispose(),n.dtype==="float32"){if(d<0||b>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but got range [${d} - ${b}].`)}else if(n.dtype==="int32"){if(d<0||b>255)throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but got range [${d} - ${b}].`)}else throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);const x=n.dtype==="float32"?255:1,w=new Uint8ClampedArray(o*r*4);for(let L=0;L<r*o;++L){let S,I,N,A;i===1?(S=a[L]*x,I=a[L]*x,N=a[L]*x,A=255):i===3?(S=a[L*3]*x,I=a[L*3+1]*x,N=a[L*3+2]*x,A=255):i===4&&(S=a[L*4]*x,I=a[L*4+1]*x,N=a[L*4+2]*x,A=a[L*4+3]*x);const E=L*4;w[E+0]=Math.round(S),w[E+1]=Math.round(I),w[E+2]=Math.round(N),w[E+3]=Math.round(A)}if(t!=null){t.width=o,t.height=r;const L=t.getContext("2d"),S=new ImageData(w,o,r);L.putImageData(S,0,0)}return n!==e&&n.dispose(),w}const wI=m({fromPixels_:gI});const lf={};Re(lf,{prepareAndValidate:()=>hf});function hf(e,t){if(e.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[t.rank-1]>e.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[t.rank-1]} vs. ${e.rank}`);if(e.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);const n=t.shape,r=n[n.length-1];let o=1;for(let p=0;p<n.length-1;++p)o*=n[p];const i=e.shape,a=n.slice();a.pop();let s=1;for(let p=r;p<e.rank;++p)s*=i[p],a.push(i[p]);const c=[...Gt(e.shape).map(p=>p/s),1].slice(0,r);return[a,o,s,c]}const uf={};Re(uf,{Serializable:()=>Yh,SerializationMap:()=>ir,registerClass:()=>nt});class Yh{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class ir{constructor(){this.classNameMap={}}static getMap(){return ir.instance==null&&(ir.instance=new ir()),ir.instance}static register(e){ir.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function nt(e){f(e.className!=null,()=>"Class being registered does not have the static className property defined."),f(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),f(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ir.register(e)}const xI="2.3.0";class ot extends Yh{minimize(e,t=!1,n){const{value:r,grads:o}=this.computeGradients(e,n);if(n!=null){const i=n.map(a=>({name:a.name,tensor:o[a.name]}));this.applyGradients(i)}else this.applyGradients(o);return ve(o),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return zl(e,t)}dispose(){this.iterations_!=null&&ve(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:_(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(ot,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class sr extends ot{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=g.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=g.registeredVariables[n],i=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${n}/accum_grad`,variable:k(()=>W(o).variable(i))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${n}/accum_var`,variable:k(()=>W(o).variable(i))});const a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;const s=this.accumulatedGrads[r].variable,c=this.accumulatedUpdates[r].variable;k(()=>{const p=R(v(s,this.rho),v(H(a),1-this.rho)),l=v(F(me(R(c,this.epsilon)),me(R(s,this.epsilon))),a),h=R(v(c,this.rho),v(H(l),1-this.rho));s.assign(p),c.assign(h);const d=R(v(l,-this.learningRate),o);o.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ve(this.accumulatedGrads.map(e=>e.variable)),ve(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}sr.className="Adadelta";nt(sr);class ar extends ot{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=g.registeredVariables[n];if(this.accumulatedGrads[r]==null){const s=!1;this.accumulatedGrads[r]={originalName:`${n}/accumulator`,variable:k(()=>Ge(o.shape,this.initialAccumulatorValue).variable(s))}}const i=Array.isArray(e)?e[r].tensor:e[n];if(i==null)return;const a=this.accumulatedGrads[r].variable;k(()=>{const s=R(a,H(i));a.assign(s);const c=R(v(F(i,me(R(s,g.backend.epsilon()))),-this.learningRate),o);o.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ve(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)}}ar.className="Adagrad";nt(ar);class cr extends ot{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],k(()=>{this.accBeta1=_(t).variable(),this.accBeta2=_(n).variable()}),r==null&&(this.epsilon=g.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);k(()=>{const n=O(1,this.accBeta1),r=O(1,this.accBeta2);t.forEach((o,i)=>{const a=g.registeredVariables[o],s=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${o}/m`,variable:k(()=>W(a).variable(s))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${o}/v`,variable:k(()=>W(a).variable(s))});const c=Array.isArray(e)?e[i].tensor:e[o];if(c==null)return;const p=this.accumulatedFirstMoment[i].variable,l=this.accumulatedSecondMoment[i].variable,h=R(v(p,this.beta1),v(c,1-this.beta1)),d=R(v(l,this.beta2),v(H(c),1-this.beta2)),b=F(h,n),x=F(d,r);p.assign(h),l.assign(d);const w=R(v(F(b,R(me(x),this.epsilon)),-this.learningRate),a);a.assign(w)}),this.accBeta1.assign(v(this.accBeta1,this.beta1)),this.accBeta2.assign(v(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ve(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ve(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),k(()=>{this.accBeta1.assign(Je(this.beta1,this.iterations_+1)),this.accBeta2.assign(Je(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.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)}}cr.className="Adam";nt(cr);class pr extends ot{constructor(e,t,n,r=null,o=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=o,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],k(()=>{this.iteration=_(0).variable(),this.accBeta1=_(t).variable()}),r==null&&(this.epsilon=g.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);k(()=>{const n=O(1,this.accBeta1),r=F(-this.learningRate,R(v(this.iteration,this.decay),1));t.forEach((o,i)=>{const a=g.registeredVariables[o],s=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${o}/m`,variable:W(a).variable(s)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${o}/v`,variable:W(a).variable(s)});const c=Array.isArray(e)?e[i].tensor:e[o];if(c==null)return;const p=this.accumulatedFirstMoment[i].variable,l=this.accumulatedWeightedInfNorm[i].variable,h=R(v(p,this.beta1),v(c,1-this.beta1)),d=v(l,this.beta2),b=Ee(c),x=kt(d,b);p.assign(h),l.assign(x);const w=R(v(F(r,n),F(h,R(x,this.epsilon))),a);a.assign(w)}),this.iteration.assign(R(this.iteration,1)),this.accBeta1.assign(v(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ve(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ve(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)}}pr.className="Adamax";nt(pr);class on extends ot{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,r)=>{const o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;const i=g.registeredVariables[n];k(()=>{const a=R(v(this.c,o),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=al(_(-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)}}on.className="SGD";nt(on);class lr extends on{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=_(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=g.registeredVariables[n];if(this.accumulations[r]==null){const s=!1;this.accumulations[r]={originalName:`${n}/momentum`,variable:k(()=>W(o).variable(s))}}const i=this.accumulations[r].variable,a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;k(()=>{let s;const c=R(v(this.m,i),a);this.useNesterov?s=R(v(this.c,R(a,v(c,this.m))),o):s=R(v(this.c,c),o),i.assign(c),o.assign(s)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ve(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}lr.className="Momentum";nt(lr);class hr extends ot{constructor(e,t=.9,n=0,r=null,o=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=o,r==null&&(this.epsilon=g.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,r)=>{const o=g.registeredVariables[n],i=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:k(()=>W(o).variable(i))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:k(()=>W(o).variable(i))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:k(()=>W(o).variable(i))});const a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;const s=this.accumulatedMeanSquares[r].variable,c=this.accumulatedMoments[r].variable;k(()=>{const p=R(v(s,this.decay),v(H(a),1-this.decay));if(this.centered){const l=this.accumulatedMeanGrads[r].variable,h=R(v(l,this.decay),v(a,1-this.decay)),d=F(v(a,this.learningRate),me(O(p,R(H(h),this.epsilon)))),b=R(v(c,this.momentum),d);s.assign(p),l.assign(h),c.assign(b);const x=O(o,b);o.assign(x)}else{const l=R(v(s,this.decay),v(H(a),1-this.decay)),h=R(v(c,this.momentum),F(v(a,this.learningRate),me(R(l,this.epsilon))));s.assign(l),c.assign(h);const d=O(o,h);o.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ve(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ve(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ve(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(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.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)}}hr.className="RMSProp";nt(hr);class Tn{static sgd(e){return new on(e)}static momentum(e,t,n=!1){return new lr(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,o=!1){return new hr(e,t,n,r,o)}static adam(e=.001,t=.9,n=.999,r=null){return new cr(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new sr(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,o=0){return new pr(e,t,n,r,o)}static adagrad(e,t=.1){return new ar(e,t)}}lr,on,sr,ar,hr,pr,cr;const yI={sgd:Tn.sgd,momentum:Tn.momentum,adadelta:Tn.adadelta,adagrad:Tn.adagrad,rmsprop:Tn.rmsprop,adamax:Tn.adamax,adam:Tn.adam};const LI=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function vI(){return new Promise(e=>LI(()=>e()))}function SI(e,t,n){const r=n*(typeof e=="number"?e:e[0]),o=t*(typeof e=="number"?e:e[1]);return[r,o]}function II(e,t,n,r=!0){let o=[];if(r)o=o.concat(t.slice(0)),o.push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);const i=t.length;for(let a=0;a<i;++a)o=o.concat([e[a+1]/t[a],t[a]]);o=o.concat(e.slice(i+1))}return o}function TI(e,t,n=!0){const r=[];if(n){r.push(t);for(let o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{const o=[],i=[];for(let a=1;a<e;++a)a>=t*2+1||a%2===1?i.push(a):o.push(a);r.push(...o),r.push(0),r.push(...i)}return r}function AI(e,t,n,r=!0){const o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(let i=1;i<e.length;++i)i<=t.length?r?o.push(t[i-1]*e[i]):o.push(e[i]/t[i-1]):o.push(e[i]);return o}function NI(e,t){const n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function RI(e,t,n){const r=e.slice(0,1);for(let o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}const Vh=1.7580993408473768,Kh=1.0507009873554805;const _I=.3275911,CI=.254829592,EI=-.284496736,OI=1.421413741,kI=-1.453152027,DI=1.061405429;function FI(...e){pe().getBool("IS_TEST")||console.warn(...e)}function MI(...e){pe().getBool("IS_TEST")||console.log(...e)}function UI(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);const n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function WI(e){const t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function BI(e){const t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function $I(e){const t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function jI(e,t){const n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function GI(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function PI(e,t){const n=new Float32Array(e/2),r=new Float32Array(e/2);for(let o=0;o<Math.ceil(e/2);o++){const i=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(i),r[o]=Math.sin(i)}return{real:n,imag:r}}function qI(e,t,n){const r=(n?2:-2)*Math.PI*(e/t),o=Math.cos(r),i=Math.sin(r);return{real:o,imag:i}}const df={};Re(df,{ERF_A1:()=>CI,ERF_A2:()=>EI,ERF_A3:()=>OI,ERF_A4:()=>kI,ERF_A5:()=>DI,ERF_P:()=>_I,PARALLELIZE_THRESHOLD:()=>Da,SELU_SCALE:()=>Kh,SELU_SCALEALPHA:()=>Vh,applyActivation:()=>rr,assertAndGetBroadcastShape:()=>q,assertAxesAreInnerMostDims:()=>zw,assertParamsConsistent:()=>pl,assignToTypedArray:()=>GI,axesAreInnerMostDims:()=>Vp,calculateShapes:()=>mm,castTensor:()=>HI,combineLocations:()=>Bd,complexWithEvenIndex:()=>BI,complexWithOddIndex:()=>$I,computeConv2DInfo:()=>je,computeConv3DInfo:()=>Ln,computeDefaultPad:()=>sl,computeDilation2DInfo:()=>ix,computeOptimalWindowSize:()=>_y,computeOutAndReduceShapes:()=>Kp,computeOutShape:()=>ll,computePool2DInfo:()=>_t,computePool3DInfo:()=>Qt,convertConv2DDataFormat:()=>yn,eitherStridesOrDilationsAreOne:()=>le,expandShapeToKeepDim:()=>we,exponent:()=>qI,exponents:()=>PI,getAxesPermutation:()=>ge,getBroadcastDims:()=>my,getComplexWithIndex:()=>jI,getFusedBiasGradient:()=>nr,getFusedDyActivation:()=>tr,getImageCenter:()=>SI,getInnerMostAxes:()=>De,getPermuted:()=>TI,getReductionAxes:()=>ae,getReshaped:()=>II,getReshapedPermuted:()=>AI,getSliceBeginCoords:()=>NI,getSliceSize:()=>RI,getUndoAxesPermutation:()=>Un,linspaceImpl:()=>YI,log:()=>MI,mergeRealAndImagArrays:()=>UI,prepareAndValidate:()=>hf,prepareSplitSize:()=>yh,reshapeTensor:()=>zI,segment_util:()=>qd,shouldFuse:()=>or,splitRealAndImagArrays:()=>WI,tupleValuesAreOne:()=>ct,upcastType:()=>Mr,validateInput:()=>Ba,validateUpdateShape:()=>Fh,warn:()=>FI});function HI(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();const r=Ce(e.shape),o=C(e,"float32"),i=n.complex(o,r);return r.dispose(),o.dispose(),i}if(!kp(e.dtype,t))return g.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){const r=n.real(e),o=C(r,t);return r.dispose(),o}if(t==="int32")return n.int(e);if(t==="bool"){const r=_(0,e.dtype),o=n.notEqual(e,r);return r.dispose(),o}else throw new Error(`Error in Cast: failed to cast ${e.dtype} to ${t}`)}function zI(e,t){return g.makeTensorFromDataId(e.dataId,t,e.dtype)}function YI(e,t,n){const r=(t-e)/(n-1),o=Pt(n,"float32");o[0]=e;for(let i=1;i<o.length;i++)o[i]=o[i-1]+r;return ce(o,"float32")}function mf(e,t,n){const r=new Array(e.rank).fill(0),o=e.shape.slice();return t.map(i=>{const a=[...o];a[n]=i;const s=G(e,r,a);return r[n]+=i,s})}function ff(e,t){const n=new Array(e.rank);for(let o=0;o<n.length;o++)n[o]=e.shape[o]*t[o];const r=Be(n,e.dtype);for(let o=0;o<r.values.length;++o){const i=r.indexToLoc(o),a=new 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n=t.length-1;n>0;n--){const r=Math.floor(Math.random()*(n+1)),o=t[n];t[n]=t[r],t[r]=o}return t}function Lr(e){return 1/(1+Math.exp(-e))}function $u(e){return Math.log(e/(1-e))}class Ar extends de{constructor(e,t,n,r,o=!1){super({x:e,y:t,width:n,height:r},o)}}const aT=.5,cT=.43,pT=.45;class st{constructor(e,t,n=new J(0,0)){const{width:r,height:o}=t;this._imgDims=new Qe(r,o),this._shift=n,this._positions=e.map(i=>i.mul(new J(r,o)).add(n))}get shift(){return new J(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(e=>e.sub(this._shift).div(new J(this.imageWidth,this.imageHeight)))}forSize(e,t){return new this.constructor(this.relativePositions,{width:e,height:t})}shiftBy(e,t){return new this.constructor(this.relativePositions,this._imgDims,new J(e,t))}shiftByPoint(e){return this.shiftBy(e.x,e.y)}align(e,t={}){if(e){const o=e instanceof fe?e.box.floor():new de(e);return this.shiftBy(o.x,o.y).align(null,t)}const{useDlibAlignment:n,minBoxPadding:r}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},t);return n?this.alignDlib():this.alignMinBbox(r)}alignDlib(){const e=this.getRefPointsForAlignment(),[t,n,r]=e,o=l=>r.sub(l).magnitude(),i=(o(t)+o(n))/2,a=Math.floor(i/pT),s=mr(e),c=Math.floor(Math.max(0,s.x-aT*a)),p=Math.floor(Math.max(0,s.y-cT*a));return new Ar(c,p,Math.min(a,this.imageWidth+c),Math.min(a,this.imageHeight+p))}alignMinBbox(e){const t=na(this.positions);return t.pad(t.width*e,t.height*e)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class md extends st{getRefPointsForAlignment(){const e=this.positions;return[e[0],e[1],mr([e[3],e[4]])]}}class Nr extends st{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(mr)}}class wo{constructor(e,t){this._label=e,this._distance=t}get label(){return this._label}get distance(){return this._distance}toString(e=!0){return`${this.label}${e?` (${dr(this.distance)})`:""}`}}class bo extends de{constructor(e,t){super(e);this._label=t}static assertIsValidLabeledBox(e,t){if(de.assertIsValidBox(e,t),!Mt(e.label))throw new Error(`${t} - expected property label (${e.label}) to be a number`)}get label(){return this._label}}class xn{constructor(e,t){if(!(typeof e=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(t)||t.some(n=>!(n instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=e,this._descriptors=t}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(e=>Array.from(e))}}static fromJSON(e){const t=e.descriptors.map(n=>new Float32Array(n));return new xn(e.label,t)}}class dd extends bo{constructor(e,t,n,r){super(e,t);this._score=n,this._classScore=r}static assertIsValidPredictedBox(e,t){if(bo.assertIsValidLabeledBox(e,t),!Xr(e.score)||!Xr(e.classScore))throw new Error(`${t} - expected properties score (${e.score}) and (${e.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}}function Nt(e){return e.detection instanceof fe}function mn(e,t){const n={detection:t};return Object.assign({},e,n)}function ru(){const e=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},t=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:t}}function ec(e){let t="";if(!e)try{e=require("fs")}catch(r){t=r.toString()}const n=e?function(r){return new Promise((o,i)=>{e.readFile(r,function(a,s){return a?i(a):o(s)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)};return{readFile:n}}function ou(){const e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=function(){if(e)return new e();throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=function(){if(t)return new t();throw new Error("createImageElement - missing Image implementation for nodejs environment")},o=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},i=ec();return zs({Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:n,createImageElement:r,fetch:o},i)}function iu(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const su=fu(ku());let Fe;function lT(){if(!Fe)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Fe}function au(e){Fe=e}function cu(){if(iu())return au(ru());if(su.isNodejs())return au(ou())}function hT(e){if(Fe||cu(),!Fe)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:t=Fe.Canvas,Image:n=Fe.Image}=e;Fe.Canvas=t,Fe.Image=n,Fe.createCanvasElement=e.createCanvasElement||(()=>new t()),Fe.createImageElement=e.createImageElement||(()=>new n()),Fe.ImageData=e.ImageData||Fe.ImageData,Fe.Video=e.Video||Fe.Video,Fe.fetch=e.fetch||Fe.fetch,Fe.readFile=e.readFile||Fe.readFile}const se={getEnv:lT,setEnv:au,initialize:cu,createBrowserEnv:ru,createFileSystem:ec,createNodejsEnv:ou,monkeyPatch:hT,isBrowser:iu,isNodejs:su.isNodejs};cu();function gn(e){return!se.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function qe(e){const{Canvas:t,CanvasRenderingContext2D:n}=se.getEnv();if(e instanceof n)return e;const r=gn(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const o=r.getContext("2d");if(!o)throw new Error("resolveContext2d - canvas 2d context is null");return o}var Kt;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Kt||(Kt={}));class tc{constructor(e={}){const{anchorPosition:t,backgroundColor:n,fontColor:r,fontSize:o,fontStyle:i,padding:a}=e;this.anchorPosition=t||Kt.TOP_LEFT,this.backgroundColor=n||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=o||14,this.fontStyle=i||"Georgia",this.padding=a||4}}class Zr{constructor(e,t,n={}){this.text=typeof e=="string"?[e]:e instanceof Zr?e.text:e,this.anchor=t,this.options=new tc(n)}measureWidth(e){const{padding:t}=this.options;return this.text.map(n=>e.measureText(n).width).reduce((n,r)=>n<r?r:n,0)+2*t}measureHeight(){const{fontSize:e,padding:t}=this.options;return this.text.length*e+2*t}getUpperLeft(e,t){const{anchorPosition:n}=this.options,r=n===Kt.BOTTOM_RIGHT||n===Kt.TOP_RIGHT,o=n===Kt.BOTTOM_LEFT||n===Kt.BOTTOM_RIGHT,i=this.measureWidth(e),a=this.measureHeight(),s=r?this.anchor.x-i:this.anchor.x,c=o?this.anchor.y-a:this.anchor.y;if(t){const{width:p,height:l}=t,h=Math.max(Math.min(s,p-i),0),d=Math.max(Math.min(c,l-a),0);return{x:h,y:d}}return{x:s,y:c}}draw(e){const t=gn(e),n=qe(t),{backgroundColor:r,fontColor:o,fontSize:i,fontStyle:a,padding:s}=this.options;n.font=`${i}px ${a}`;const c=this.measureWidth(n),p=this.measureHeight();n.fillStyle=r;const l=this.getUpperLeft(n,t);n.fillRect(l.x,l.y,c,p),n.fillStyle=o,this.text.forEach((h,d)=>{const b=s+l.x,x=s+l.y+(d+1)*i;n.fillText(h,b,x)})}}class cb{constructor(e={}){const{boxColor:t,lineWidth:n,label:r,drawLabelOptions:o}=e;this.boxColor=t||"rgba(0, 0, 255, 1)",this.lineWidth=n||2,this.label=r;const i={anchorPosition:Kt.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new tc(Object.assign({},i,o))}}class pu{constructor(e,t={}){this.box=new de(e),this.options=new cb(t)}draw(e){const t=qe(e),{boxColor:n,lineWidth:r}=this.options,{x:o,y:i,width:a,height:s}=this.box;t.strokeStyle=n,t.lineWidth=r,t.strokeRect(o,i,a,s);const{label:c}=this.options;c&&new Zr([c],{x:o-r/2,y:i},this.options.drawLabelOptions).draw(e)}}function uT(e,t){const n=Array.isArray(t)?t:[t];n.forEach(r=>{const o=r instanceof fe?r.score:Nt(r)?r.detection.score:void 0,i=r instanceof fe?r.box:Nt(r)?r.detection.box:new de(r),a=o?`${dr(o)}`:void 0;new pu(i,{label:a}).draw(e)})}function Ir(e){const{Image:t,Video:n}=se.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function ya(e){return new Promise((t,n)=>{if(e instanceof se.getEnv().Canvas||Ir(e))return t();function r(i){if(!i.currentTarget)return;i.currentTarget.removeEventListener("load",r),i.currentTarget.removeEventListener("error",o),t(i)}function o(i){if(!i.currentTarget)return;i.currentTarget.removeEventListener("load",r),i.currentTarget.removeEventListener("error",o),n(i)}e.addEventListener("load",r),e.addEventListener("error",o)})}function xa(e){return new Promise((t,n)=>{if(!(e instanceof Blob))return n("bufferToImage - expected buf to be of type: Blob");const r=new FileReader();r.onload=()=>{if(typeof r.result!="string")return n("bufferToImage - expected reader.result to be a string, in onload");const o=se.getEnv().createImageElement();o.onload=()=>t(o),o.onerror=n,o.src=r.result},r.onerror=n,r.readAsDataURL(e)})}function bn(e){const{Image:t,Video:n}=se.getEnv();return e instanceof t?new Qe(e.naturalWidth,e.naturalHeight):e instanceof n?new Qe(e.videoWidth,e.videoHeight):new Qe(e.width,e.height)}function En({width:e,height:t}){const{createCanvasElement:n}=se.getEnv(),r=n();return r.width=e,r.height=t,r}function Tr(e,t){const{ImageData:n}=se.getEnv();if(!(e instanceof n)&&!Ir(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:r,height:o}=t||bn(e),i=En({width:r,height:o});return e instanceof n?qe(i).putImageData(e,0,0):qe(i).drawImage(e,0,0,r,o),i}async function ba(e,t){const n=t||se.getEnv().createCanvasElement(),[r,o,i]=e.shape.slice(gt(e)?1:0),a=k(()=>e.as3D(r,o,i).toInt());return await Kr.toPixels(a,n),a.dispose(),n}function go(e){const{Image:t,Canvas:n,Video:r}=se.getEnv();return e instanceof t||e instanceof n||e instanceof r}function ga(e,t,n=!1){const{Image:r,Canvas:o}=se.getEnv();if(!(e instanceof r||e instanceof o))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");const i=bn(e),a=t/Math.max(i.height,i.width),s=a*i.width,c=a*i.height,p=En({width:t,height:t}),l=e instanceof o?e:Tr(e),h=Math.abs(s-c)/2,d=n&&s<c?h:0,b=n&&c<s?h:0;return qe(p).drawImage(l,d,b,s,c),p}class Jt{constructor(e,t=!1){if(this._imageTensors=[],this._canvases=[],this._treatAsBatchInput=!1,this._inputDimensions=[],!Array.isArray(e))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${e}`);this._treatAsBatchInput=t,this._batchSize=e.length,e.forEach((n,r)=>{if(an(n)){this._imageTensors[r]=n,this._inputDimensions[r]=n.shape;return}if(gt(n)){const i=n.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=n,this._inputDimensions[r]=n.shape.slice(1);return}const o=n instanceof se.getEnv().Canvas?n:Tr(n);this._canvases[r]=o,this._inputDimensions[r]=[o.height,o.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return Vt(this.batchSize,0,1).map((e,t)=>this.getReshapedInputDimensions(t))}getInput(e){return this.canvases[e]||this.imageTensors[e]}getInputDimensions(e){return this._inputDimensions[e]}getInputHeight(e){return this._inputDimensions[e][0]}getInputWidth(e){return this._inputDimensions[e][1]}getReshapedInputDimensions(e){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");const t=this.getInputWidth(e),n=this.getInputHeight(e);return nu({width:t,height:n},this.inputSize)}toBatchTensor(e,t=!0){return this._inputSize=e,k(()=>{const n=Vt(this.batchSize,0,1).map(o=>{const i=this.getInput(o);if(i instanceof ee){let a=gt(i)?i:i.expandDims();return a=ea(a,t),(a.shape[1]!==e||a.shape[2]!==e)&&(a=Va.resizeBilinear(a,[e,e])),a.as3D(e,e,3)}if(i instanceof se.getEnv().Canvas)return Kr.fromPixels(ga(i,e,t));throw new Error(`toBatchTensor - at batchIdx ${o}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)}),r=ke(n.map(o=>o.toFloat())).as4D(this.batchSize,e,e,3);return r})}}async function ue(e){if(e instanceof Jt)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");const n=o=>Array.isArray(e)?` at input index ${o}:`:"",r=t.map(gn);return r.forEach((o,i)=>{if(!go(o)&&!an(o)&&!gt(o))throw typeof t[i]=="string"?new Error(`toNetInput -${n(i)} string passed, but could not resolve HTMLElement for element id ${t[i]}`):new Error(`toNetInput -${n(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(gt(o)){const a=o.shape[0];if(a!==1)throw new Error(`toNetInput -${n(i)} tf.Tensor4D with batchSize ${a} passed, but not supported in input array`)}}),await Promise.all(r.map(o=>go(o)&&ya(o))),new Jt(r,Array.isArray(e))}async function _n(e,t){const{Canvas:n}=se.getEnv();let r=e;if(!(e instanceof n)){const a=await ue(e);if(a.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const s=a.getInput(0);r=s instanceof n?s:await ba(s)}const o=qe(r),i=t.map(a=>a instanceof fe?a.forSize(r.width,r.height).box.floor():a).map(a=>a.clipAtImageBorders(r.width,r.height));return i.map(({x:a,y:s,width:c,height:p})=>{const l=En({width:c,height:p});return qe(l).putImageData(o.getImageData(a,s,c,p),0,0),l})}async function Cn(e,t){if(!an(e)&&!gt(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(gt(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return k(()=>{const[n,r,o]=e.shape.slice(gt(e)?1:0),i=t.map(s=>s instanceof fe?s.forSize(r,n).box:s).map(s=>s.clipAtImageBorders(r,n)),a=i.map(({x:s,y:c,width:p,height:l})=>Os(e.as3D(n,r,o),[c,s,0],[l,p,o]));return a})}async function wn(e,t){const n=se.getEnv().fetch,r=await n(e,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function ud(e){const t=await wn(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return xa(n)}async function wa(e){return(await wn(e)).json()}async function hd(e){return new Float32Array(await(await wn(e)).arrayBuffer())}function nc(e,t){const n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};const r=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(r,"");const o=e.split("/").filter(s=>s),i=e.endsWith(".json")?o[o.length-1]:n;let a=r+(e.endsWith(".json")?o.slice(0,o.length-1):o).join("/");return a=e.startsWith("/")?`/${a}`:a,{modelBaseUri:a,manifestUri:a==="/"?`/${i}`:`${a}/${i}`}}async function fa(e,t){const{manifestUri:n,modelBaseUri:r}=nc(e,t);let o=await wa(n);return Vr.loadWeights(o,r)}function ld(e,t,n=!1){const{width:r,height:o}=n?bn(t):t;return e.width=r,e.height=o,{width:r,height:o}}class Ue{constructor(e){this._name=e,this._params=void 0,this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(e){const{obj:t,objProp:n}=this.traversePropertyPath(e);return t[n]}reassignParamFromPath(e,t){const{obj:n,objProp:r}=this.traversePropertyPath(e);n[r].dispose(),n[r]=t}getParamList(){return this._paramMappings.map(({paramPath:e})=>({path:e,tensor:this.getParamFromPath(e)}))}getTrainableParams(){return this.getParamList().filter(e=>e.tensor instanceof Ht)}getFrozenParams(){return this.getParamList().filter(e=>!(e.tensor instanceof Ht))}variable(){this.getFrozenParams().forEach(({path:e,tensor:t})=>{this.reassignParamFromPath(e,t.variable())})}freeze(){this.getTrainableParams().forEach(({path:e,tensor:t})=>{const n=ze(t.dataSync());t.dispose(),this.reassignParamFromPath(e,n)})}dispose(e=!0){this.getParamList().forEach(t=>{if(e&&t.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${t.path}`);t.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:e})=>Array.from(e.dataSync())).reduce((e,t)=>e.concat(t)))}async load(e){if(e instanceof Float32Array){this.extractWeights(e);return}await this.loadFromUri(e)}async loadFromUri(e){if(e&&typeof e!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const t=await fa(e,this.getDefaultModelName());this.loadFromWeightMap(t)}async loadFromDisk(e){if(e&&typeof e!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:t}=se.getEnv(),{manifestUri:n,modelBaseUri:r}=nc(e,this.getDefaultModelName()),o=c=>Promise.all(c.map(p=>t(p).then(l=>l.buffer))),i=Vr.weightsLoaderFactory(o),a=JSON.parse((await t(n)).toString()),s=await i(a,r);this.loadFromWeightMap(s)}loadFromWeightMap(e){const{paramMappings:t,params:n}=this.extractParamsFromWeigthMap(e);this._paramMappings=t,this._params=n}extractWeights(e){const{paramMappings:t,params:n}=this.extractParams(e);this._paramMappings=t,this._params=n}traversePropertyPath(e){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const t=e.split("/").reduce((o,i)=>{if(!o.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${e}`);return{obj:o.nextObj,objProp:i,nextObj:o.nextObj[i]}},{nextObj:this.params}),{obj:n,objProp:r}=t;if(!n||!r||!(n[r]instanceof ee))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${e}`);return{obj:n,objProp:r}}}function rt(e,t,n){return k(()=>{let r=Xn(e,t.depthwise_filter,t.pointwise_filter,n,"same");return r=R(r,t.bias),r})}function rc(e,t,n=!1){return k(()=>{const r=ie(n?R(xe(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):rt(e,t.conv0,[2,2])),o=rt(r,t.conv1,[1,1]),i=ie(R(r,o)),a=rt(i,t.conv2,[1,1]);return ie(R(r,R(o,a)))})}function Ws(e,t,n=!1,r=!0){return k(()=>{const o=ie(n?R(xe(e,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):rt(e,t.conv0,r?[2,2]:[1,1])),i=rt(o,t.conv1,[1,1]),a=ie(R(o,i)),s=rt(a,t.conv2,[1,1]),c=ie(R(o,R(i,s))),p=rt(c,t.conv3,[1,1]);return ie(R(o,R(i,R(s,p))))})}function fr(e,t,n="same",r=!1){return k(()=>{const o=R(xe(e,t.filters,[1,1],n),t.bias);return r?ie(o):o})}function Xe(e,t){Object.keys(e).forEach(n=>{t.some(r=>r.originalPath===n)||e[n].dispose()})}function Qr(e,t){return function(n,r,o,i){const a=ft(e(n*r*o*o),[o,o,n,r]),s=ce(e(r));return t.push({paramPath:`${i}/filters`},{paramPath:`${i}/bias`}),{filters:a,bias:s}}}function oc(e,t){return function(n,r,o){const i=mt(e(n*r),[n,r]),a=ce(e(r));return t.push({paramPath:`${o}/weights`},{paramPath:`${o}/bias`}),{weights:i,bias:a}}}class lu{constructor(e,t,n){this.depthwise_filter=e,this.pointwise_filter=t,this.bias=n}}function eo(e,t){return function(n,r,o){const i=ft(e(3*3*n),[3,3,n,1]),a=ft(e(n*r),[1,1,n,r]),s=ce(e(r));return t.push({paramPath:`${o}/depthwise_filter`},{paramPath:`${o}/pointwise_filter`},{paramPath:`${o}/bias`}),new lu(i,a,s)}}function to(e){return function(t){const n=e(`${t}/depthwise_filter`,4),r=e(`${t}/pointwise_filter`,4),o=e(`${t}/bias`,1);return new lu(n,r,o)}}function it(e,t){return function(n,r,o){const i=e[n];if(!ur(i,r))throw new Error(`expected weightMap[${n}] to be a Tensor${r}D, instead have ${i}`);return t.push({originalPath:n,paramPath:o||n}),i}}function Ze(e){let t=e;function n(o){const i=t.slice(0,o);return t=t.slice(o),i}function r(){return t}return{extractWeights:n,getRemainingWeights:r}}function ic(e,t){const n=Qr(e,t),r=eo(e,t);function o(a,s,c,p=!1){const l=p?n(a,s,3,`${c}/conv0`):r(a,s,`${c}/conv0`),h=r(s,s,`${c}/conv1`),d=r(s,s,`${c}/conv2`);return{conv0:l,conv1:h,conv2:d}}function i(a,s,c,p=!1){const{conv0:l,conv1:h,conv2:d}=o(a,s,c,p),b=r(s,s,`${c}/conv3`);return{conv0:l,conv1:h,conv2:d,conv3:b}}return{extractDenseBlock3Params:o,extractDenseBlock4Params:i}}function pb(e){const t=[],{extractWeights:n,getRemainingWeights:r}=Ze(e),{extractDenseBlock4Params:o}=ic(n,t),i=o(3,32,"dense0",!0),a=o(32,64,"dense1"),s=o(64,128,"dense2"),c=o(128,256,"dense3");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:a,dense2:s,dense3:c}}}function sc(e){return function(t){const n=e(`${t}/filters`,4),r=e(`${t}/bias`,1);return{filters:n,bias:r}}}function ac(e,t){const n=it(e,t),r=sc(n),o=to(n);function i(s,c=!1){const p=c?r(`${s}/conv0`):o(`${s}/conv0`),l=o(`${s}/conv1`),h=o(`${s}/conv2`);return{conv0:p,conv1:l,conv2:h}}function a(s,c=!1){const p=c?r(`${s}/conv0`):o(`${s}/conv0`),l=o(`${s}/conv1`),h=o(`${s}/conv2`),d=o(`${s}/conv3`);return{conv0:p,conv1:l,conv2:h,conv3:d}}return{extractDenseBlock3Params:i,extractDenseBlock4Params:a}}function lb(e){const t=[],{extractDenseBlock4Params:n}=ac(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return Xe(e,t),{params:r,paramMappings:t}}class cc extends Ue{constructor(){super("FaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceFeatureExtractor - load model before inference");return k(()=>{const n=e.toBatchTensor(112,!0),r=[122.782,117.001,104.298],o=wt(n,r).div(_(255));let i=Ws(o,t.dense0,!0);return i=Ws(i,t.dense1),i=Ws(i,t.dense2),i=Ws(i,t.dense3),i=pt(i,[7,7],[2,2],"valid"),i})}async forward(e){return this.forwardInput(await ue(e))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(e){return lb(e)}extractParams(e){return pb(e)}}function Bs(e,t){return k(()=>R(Y(e,t.weights),t.bias))}function hb(e,t,n){const r=[],{extractWeights:o,getRemainingWeights:i}=Ze(e),a=oc(o,r),s=a(t,n,"fc");if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{paramMappings:r,params:{fc:s}}}function ub(e){const t=[],n=it(e,t);function r(i){const a=n(`${i}/weights`,2),s=n(`${i}/bias`,1);return{weights:a,bias:s}}const o={fc:r("fc")};return Xe(e,t),{params:o,paramMappings:t}}function pc(e){const t={},n={};return Object.keys(e).forEach(r=>{const o=r.startsWith("fc")?n:t;o[r]=e[r]}),{featureExtractorMap:t,classifierMap:n}}class lc extends Ue{constructor(e,t){super(e);this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return k(()=>{const n=e instanceof Jt?this.faceFeatureExtractor.forwardInput(e):e;return Bs(n.as2D(n.shape[0],-1),t.fc)})}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:n}=this.extractClassifierParams(e);this._params=t,this._paramMappings=n}extractClassifierParams(e){return hb(e,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:n}=pc(e);return this.faceFeatureExtractor.loadFromWeightMap(t),ub(n)}extractParams(e){const t=this.getClassifierChannelsIn(),n=this.getClassifierChannelsOut(),r=n*t+n,o=e.slice(0,e.length-r),i=e.slice(e.length-r);return this.faceFeatureExtractor.extractWeights(o),this.extractClassifierParams(i)}}const ua=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class fn{constructor(e){if(e.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${e.length}`);ua.forEach((t,n)=>{this[t]=e[n]})}asSortedArray(){return ua.map(e=>({expression:e,probability:this[e]})).sort((e,t)=>t.probability-e.probability)}}class da extends lc{constructor(e=new cc()){super("FaceExpressionNet",e)}forwardInput(e){return k(()=>rn(this.runNet(e)))}async forward(e){return this.forwardInput(await ue(e))}async predictExpressions(e){const t=await ue(e),n=await this.forwardInput(t),r=await Promise.all(Ie(n).map(async i=>{const a=await i.data();return i.dispose(),a}));n.dispose();const o=r.map(i=>new fn(i));return t.isBatchInput?o:o[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function la(e){return e.expressions instanceof fn}function uo(e,t){const n={expressions:t};return Object.assign({},e,n)}function dT(e,t,n=.1,r){const o=Array.isArray(t)?t:[t];o.forEach(i=>{const a=i instanceof fn?i:la(i)?i.expressions:void 0;if(!a)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const s=a.asSortedArray(),c=s.filter(h=>h.probability>n),p=Nt(i)?i.detection.box.bottomLeft:r||new J(0,0),l=new Zr(c.map(h=>`${h.expression} (${dr(h.probability)})`),p);l.draw(e)})}function dn(e){return Nt(e)&&e.landmarks instanceof st&&e.unshiftedLandmarks instanceof st&&e.alignedRect instanceof fe}function Rn(e,t){const{box:n}=e.detection,r=t.shiftBy(n.x,n.y),o=r.align(),{imageDims:i}=e.detection,a=new fe(e.detection.score,o.rescale(i.reverse()),i),s={landmarks:r,unshiftedLandmarks:t,alignedRect:a};return Object.assign({},e,s)}class db{constructor(e={}){const{drawLines:t=!0,drawPoints:n=!0,lineWidth:r,lineColor:o,pointSize:i,pointColor:a}=e;this.drawLines=t,this.drawPoints=n,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=o||"rgba(0, 255, 255, 1)",this.pointColor=a||"rgba(255, 0, 255, 1)"}}class mb{constructor(e,t={}){this.faceLandmarks=e,this.options=new db(t)}draw(e){const t=qe(e),{drawLines:n,drawPoints:r,lineWidth:o,lineColor:i,pointSize:a,pointColor:s}=this.options;if(n&&this.faceLandmarks instanceof Nr&&(t.strokeStyle=i,t.lineWidth=o,sn(t,this.faceLandmarks.getJawOutline()),sn(t,this.faceLandmarks.getLeftEyeBrow()),sn(t,this.faceLandmarks.getRightEyeBrow()),sn(t,this.faceLandmarks.getNose()),sn(t,this.faceLandmarks.getLeftEye(),!0),sn(t,this.faceLandmarks.getRightEye(),!0),sn(t,this.faceLandmarks.getMouth(),!0)),r){t.strokeStyle=s,t.fillStyle=s;const c=p=>{t.beginPath(),t.arc(p.x,p.y,a,0,2*Math.PI),t.fill()};this.faceLandmarks.positions.forEach(c)}}}function mT(e,t){const n=Array.isArray(t)?t:[t];n.forEach(r=>{const o=r instanceof st?r:dn(r)?r.landmarks:void 0;if(!o)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new mb(o).draw(e)})}const Ks={};Re(Ks,{AnchorPosition:()=>Kt,DrawBox:()=>pu,DrawBoxOptions:()=>cb,DrawFaceLandmarks:()=>mb,DrawFaceLandmarksOptions:()=>db,DrawTextField:()=>Zr,DrawTextFieldOptions:()=>tc,drawContour:()=>sn,drawDetections:()=>uT,drawFaceExpressions:()=>dT,drawFaceLandmarks:()=>mT});function fT(e,t){const n=Qr(e,t),r=eo(e,t);function o(a,s,c){const p=r(a,s,`${c}/separable_conv0`),l=r(s,s,`${c}/separable_conv1`),h=n(a,s,1,`${c}/expansion_conv`);return{separable_conv0:p,separable_conv1:l,expansion_conv:h}}function i(a,s){const c=r(a,a,`${s}/separable_conv0`),p=r(a,a,`${s}/separable_conv1`),l=r(a,a,`${s}/separable_conv2`);return{separable_conv0:c,separable_conv1:p,separable_conv2:l}}return{extractConvParams:n,extractSeparableConvParams:r,extractReductionBlockParams:o,extractMainBlockParams:i}}function fb(e,t){const n=[],{extractWeights:r,getRemainingWeights:o}=Ze(e),{extractConvParams:i,extractSeparableConvParams:a,extractReductionBlockParams:s,extractMainBlockParams:c}=fT(r,n),p=i(3,32,3,"entry_flow/conv_in"),l=s(32,64,"entry_flow/reduction_block_0"),h=s(64,128,"entry_flow/reduction_block_1"),d={conv_in:p,reduction_block_0:l,reduction_block_1:h},b={};Vt(t,0,1).forEach(S=>{b[`main_block_${S}`]=c(128,`middle_flow/main_block_${S}`)});const x=s(128,256,"exit_flow/reduction_block"),w=a(256,512,"exit_flow/separable_conv"),L={reduction_block:x,separable_conv:w};if(o().length!==0)throw new Error(`weights remaing after extract: ${o().length}`);return{paramMappings:n,params:{entry_flow:d,middle_flow:b,exit_flow:L}}}function gT(e,t){const n=it(e,t),r=sc(n),o=to(n);function i(s){const c=o(`${s}/separable_conv0`),p=o(`${s}/separable_conv1`),l=r(`${s}/expansion_conv`);return{separable_conv0:c,separable_conv1:p,expansion_conv:l}}function a(s){const c=o(`${s}/separable_conv0`),p=o(`${s}/separable_conv1`),l=o(`${s}/separable_conv2`);return{separable_conv0:c,separable_conv1:p,separable_conv2:l}}return{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:i,extractMainBlockParams:a}}function gb(e,t){const n=[],{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:i,extractMainBlockParams:a}=gT(e,n),s=r("entry_flow/conv_in"),c=i("entry_flow/reduction_block_0"),p=i("entry_flow/reduction_block_1"),l={conv_in:s,reduction_block_0:c,reduction_block_1:p},h={};Vt(t,0,1).forEach(w=>{h[`main_block_${w}`]=a(`middle_flow/main_block_${w}`)});const d=i("exit_flow/reduction_block"),b=o("exit_flow/separable_conv"),x={reduction_block:d,separable_conv:b};return Xe(e,n),{params:{entry_flow:l,middle_flow:h,exit_flow:x},paramMappings:n}}function bb(e,t,n){return R(xe(e,t.filters,n,"same"),t.bias)}function hu(e,t,n=!0){let r=n?ie(e):e;return r=rt(r,t.separable_conv0,[1,1]),r=rt(ie(r),t.separable_conv1,[1,1]),r=Ae(r,[3,3],[2,2],"same"),r=R(r,bb(e,t.expansion_conv,[2,2])),r}function bT(e,t){let n=rt(ie(e),t.separable_conv0,[1,1]);return n=rt(ie(n),t.separable_conv1,[1,1]),n=rt(ie(n),t.separable_conv2,[1,1]),n=R(n,e),n}class wb extends Ue{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyXception - load model before inference");return k(()=>{const n=e.toBatchTensor(112,!0),r=[122.782,117.001,104.298],o=wt(n,r).div(_(256));let i=ie(bb(o,t.entry_flow.conv_in,[2,2]));return i=hu(i,t.entry_flow.reduction_block_0,!1),i=hu(i,t.entry_flow.reduction_block_1),Vt(this._numMainBlocks,0,1).forEach(a=>{i=bT(i,t.middle_flow[`main_block_${a}`])}),i=hu(i,t.exit_flow.reduction_block),i=ie(rt(i,t.exit_flow.separable_conv,[1,1])),i})}async forward(e){return this.forwardInput(await ue(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(e){return gb(e,this._numMainBlocks)}extractParams(e){return fb(e,this._numMainBlocks)}}function xb(e){const t=[],{extractWeights:n,getRemainingWeights:r}=Ze(e),o=oc(n,t),i=o(512,1,"fc/age"),a=o(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:i,gender:a}}}}function yb(e){const t=[],n=it(e,t);function r(i){const a=n(`${i}/weights`,2),s=n(`${i}/bias`,1);return{weights:a,bias:s}}const o={fc:{age:r("fc/age"),gender:r("fc/gender")}};return Xe(e,t),{params:o,paramMappings:t}}var Bt;(function(e){e.FEMALE="female",e.MALE="male"})(Bt||(Bt={}));class La extends Ue{constructor(e=new wb(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return k(()=>{const n=e instanceof Jt?this.faceFeatureExtractor.forwardInput(e):e,r=pt(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),o=Bs(r,t.fc.age).as1D(),i=Bs(r,t.fc.gender);return{age:o,gender:i}})}forwardInput(e){return k(()=>{const{age:t,gender:n}=this.runNet(e);return{age:t,gender:rn(n)}})}async forward(e){return this.forwardInput(await ue(e))}async predictAgeAndGender(e){const t=await ue(e),n=await this.forwardInput(t),r=Ie(n.age),o=Ie(n.gender),i=r.map((s,c)=>({ageTensor:s,genderTensor:o[c]})),a=await Promise.all(i.map(async({ageTensor:s,genderTensor:c})=>{const p=(await s.data())[0],l=(await c.data())[0],h=l>.5,d=h?Bt.MALE:Bt.FEMALE,b=h?l:1-l;return s.dispose(),c.dispose(),{age:p,gender:d,genderProbability:b}}));return n.age.dispose(),n.gender.dispose(),t.isBatchInput?a:a[0]}getDefaultModelName(){return"age_gender_model"}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:n}=this.extractClassifierParams(e);this._params=t,this._paramMappings=n}extractClassifierParams(e){return xb(e)}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:n}=pc(e);return this.faceFeatureExtractor.loadFromWeightMap(t),yb(n)}extractParams(e){const t=512*1+1+(512*2+2),n=e.slice(0,e.length-t),r=e.slice(e.length-t);return this.faceFeatureExtractor.extractWeights(n),this.extractClassifierParams(r)}}class hc extends lc{postProcess(e,t,n){const r=n.map(({width:i,height:a})=>{const s=t/Math.max(a,i);return{width:i*s,height:a*s}}),o=r.length;return k(()=>{const i=(l,h)=>ke([Ge([68],l),Ge([68],h)],1).as2D(1,136).as1D(),a=(l,h)=>{const{width:d,height:b}=r[l];return h(d,b)?Math.abs(d-b)/2:0},s=l=>a(l,(h,d)=>h<d),c=l=>a(l,(h,d)=>d<h),p=e.mul(Ge([o,136],t)).sub(ke(Array.from(Array(o),(l,h)=>i(s(h),c(h))))).div(ke(Array.from(Array(o),(l,h)=>i(r[h].width,r[h].height))));return p})}forwardInput(e){return k(()=>{const t=this.runNet(e);return this.postProcess(t,e.inputSize,e.inputDimensions.map(([n,r])=>({height:n,width:r})))})}async forward(e){return this.forwardInput(await ue(e))}async detectLandmarks(e){const t=await ue(e),n=k(()=>Ie(this.forwardInput(t))),r=await Promise.all(n.map(async(o,i)=>{const a=Array.from(await o.data()),s=a.filter((p,l)=>Za(l)),c=a.filter((p,l)=>!Za(l));return new Nr(Array(68).fill(0).map((p,l)=>new J(s[l],c[l])),{height:t.getInputHeight(i),width:t.getInputWidth(i)})}));return n.forEach(o=>o.dispose()),t.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}}class Sr extends hc{constructor(e=new cc()){super("FaceLandmark68Net",e)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function Lb(e){const t=[],{extractDenseBlock3Params:n}=ac(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return Xe(e,t),{params:r,paramMappings:t}}function vb(e){const t=[],{extractWeights:n,getRemainingWeights:r}=Ze(e),{extractDenseBlock3Params:o}=ic(n,t),i=o(3,32,"dense0",!0),a=o(32,64,"dense1"),s=o(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:a,dense2:s}}}class Sb extends Ue{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyFaceFeatureExtractor - load model before inference");return k(()=>{const n=e.toBatchTensor(112,!0),r=[122.782,117.001,104.298],o=wt(n,r).div(_(255));let i=rc(o,t.dense0,!0);return i=rc(i,t.dense1),i=rc(i,t.dense2),i=pt(i,[14,14],[2,2],"valid"),i})}async forward(e){return this.forwardInput(await ue(e))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(e){return Lb(e)}extractParams(e){return vb(e)}}class ha extends hc{constructor(e=new Sb()){super("FaceLandmark68TinyNet",e)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class pd extends Sr{}function Ib(e,t){return R(v(e,t.weights),t.biases)}function uu(e,t,n,r,o="same"){const{filters:i,bias:a}=t.conv;let s=xe(e,i,n,o);return s=R(s,a),s=Ib(s,t.scale),r?ie(s):s}function Tb(e,t){return uu(e,t,[1,1],!0)}function du(e,t){return uu(e,t,[1,1],!1)}function uc(e,t){return uu(e,t,[2,2],!0,"valid")}function wT(e,t){function n(s,c,p){const l=e(s),h=l.length/(c*p*p);if(tu(h))throw new Error(`depth has to be an integer: ${h}, weights.length: ${l.length}, numFilters: ${c}, filterSize: ${p}`);return k(()=>K(ft(l,[c,h,p,p]),[2,3,1,0]))}function r(s,c,p,l){const h=n(s,c,p),d=ce(e(c));return t.push({paramPath:`${l}/filters`},{paramPath:`${l}/bias`}),{filters:h,bias:d}}function o(s,c){const p=ce(e(s)),l=ce(e(s));return t.push({paramPath:`${c}/weights`},{paramPath:`${c}/biases`}),{weights:p,biases:l}}function i(s,c,p,l){const h=r(s,c,p,`${l}/conv`),d=o(c,`${l}/scale`);return{conv:h,scale:d}}function a(s,c,p,l,h=!1){const d=i((h?.5:1)*s,c,p,`${l}/conv1`),b=i(s,c,p,`${l}/conv2`);return{conv1:d,conv2:b}}return{extractConvLayerParams:i,extractResidualLayerParams:a}}function 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t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:r}=LT(e,t),o=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!an(o))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${o}`);const i={mobilenetv1:n(),prediction_layer:r(),output_layer:{extra_dim:o}};return Xe(e,t),{params:i,paramMappings:t}}function It(e,t,n){return k(()=>{let r=xe(e,t.filters,n,"same");return r=R(r,t.batch_norm_offset),$n(r,0,6)})}const vT=.0010000000474974513;function ST(e,t,n){return k(()=>{let r=en(e,t.filters,n,"same");return r=Ct(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,vT),$n(r,0,6)})}function IT(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function Cb(e,t){return k(()=>{let n=null,r=It(e,t.conv_0,[2,2]);const o=[t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13];if(o.forEach((i,a)=>{const 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I/(d+b-I)}function AT(e){const t=Ie(K(e,[1,0])),n=[O(t[2],t[0]),O(t[3],t[1])],r=[R(t[0],F(n[0],_(2))),R(t[1],F(n[1],_(2)))];return{sizes:n,centers:r}}function NT(e,t){const{sizes:n,centers:r}=AT(e),o=Ie(K(t,[1,0])),i=F(v(Se(F(o[2],_(5))),n[0]),_(2)),a=R(v(F(o[0],_(10)),n[0]),r[0]),s=F(v(Se(F(o[3],_(5))),n[1]),_(2)),c=R(v(F(o[1],_(10)),n[1]),r[1]);return K(ke([O(a,i),O(c,s),R(a,i),R(c,s)]),[1,0])}function Ob(e,t,n){return k(()=>{const r=e.shape[0];let o=NT(y(Ot(n.extra_dim,[r,1,1]),[-1,4]),y(e,[-1,4]));o=y(o,[r,o.shape[0]/r,4]);const i=lt(G(t,[0,0,1],[-1,-1,-1]));let a=G(i,[0,0,0],[-1,-1,1]);a=y(a,[r,a.shape[1]]);const s=Ie(o),c=Ie(a);return{boxes:s,scores:c}})}function gr(e,t){return k(()=>{const n=e.shape[0],r=y(fr(e,t.box_encoding_predictor),[n,-1,1,4]),o=y(fr(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:r,classPrediction:o}})}function kb(e,t,n){return k(()=>{const r=It(e,n.conv_0,[1,1]),o=It(r,n.conv_1,[2,2]),i=It(o,n.conv_2,[1,1]),a=It(i,n.conv_3,[2,2]),s=It(a,n.conv_4,[1,1]),c=It(s,n.conv_5,[2,2]),p=It(c,n.conv_6,[1,1]),l=It(p,n.conv_7,[2,2]),h=gr(t,n.box_predictor_0),d=gr(e,n.box_predictor_1),b=gr(o,n.box_predictor_2),x=gr(a,n.box_predictor_3),w=gr(c,n.box_predictor_4),L=gr(l,n.box_predictor_5),S=Z([h.boxPredictionEncoding,d.boxPredictionEncoding,b.boxPredictionEncoding,x.boxPredictionEncoding,w.boxPredictionEncoding,L.boxPredictionEncoding],1),I=Z([h.classPrediction,d.classPrediction,b.classPrediction,x.classPrediction,w.classPrediction,L.classPrediction],1);return{boxPredictions:S,classPredictions:I}})}class bt{constructor({minConfidence:e,maxResults:t}={}){if(this._name="SsdMobilenetv1Options",this._minConfidence=e||.5,this._maxResults=t||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}}class Nn extends Ue{constructor(){super("SsdMobilenetv1")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("SsdMobilenetv1 - load model before inference");return k(()=>{const n=e.toBatchTensor(512,!1).toFloat(),r=O(v(n,_(.007843137718737125)),_(1)),o=Cb(r,t.mobilenetv1),{boxPredictions:i,classPredictions:a}=kb(o.out,o.conv11,t.prediction_layer);return Ob(i,a,t.output_layer)})}async forward(e){return this.forwardInput(await ue(e))}async locateFaces(e,t={}){const{maxResults:n,minConfidence:r}=new bt(t),o=await ue(e),{boxes:i,scores:a}=this.forwardInput(o),s=i[0],c=a[0];for(let I=1;I<i.length;I++)i[I].dispose(),a[I].dispose();const p=Array.from(await c.data()),l=.5,h=Eb(s,p,n,l,r),d=o.getReshapedInputDimensions(0),b=o.inputSize,x=b/d.width,w=b/d.height,L=s.arraySync(),S=h.map(I=>{const[N,A]=[Math.max(0,L[I][0]),Math.min(1,L[I][2])].map(D=>D*w),[E,M]=[Math.max(0,L[I][1]),Math.min(1,L[I][3])].map(D=>D*x);return new fe(p[I],new Ar(E,N,M-E,A-N),{height:o.getInputHeight(0),width:o.getInputWidth(0)})});return s.dispose(),c.dispose(),S}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(e){return _b(e)}extractParams(e){return Rb(e)}}function yc(e){const t=new Nn();return t.extractWeights(e),t}function Uu(e){return yc(e)}class Wu extends Nn{}const Db=.4,Fb=[new J(.738768,.874946),new J(2.42204,2.65704),new J(4.30971,7.04493),new J(10.246,4.59428),new J(12.6868,11.8741)],Mb=[new J(1.603231,2.094468),new J(6.041143,7.080126),new J(2.882459,3.518061),new J(4.266906,5.178857),new J(9.041765,10.66308)],Ub=[117.001,114.697,97.404],Wb="tiny_yolov2_model",Bb="tiny_yolov2_separable_conv_model";const dc=e=>typeof e=="number";function Xs(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!dc(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>dc(t.x)&&dc(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(dc)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function no(e){return k(()=>{const t=v(e,_(.10000000149011612));return R(ie(O(e,t)),t)})}function cn(e,t){return k(()=>{let n=Pe(e,[[0,0],[1,1],[1,1],[0,0]]);return n=xe(n,t.conv.filters,[1,1],"valid"),n=O(n,t.bn.sub),n=v(n,t.bn.truediv),n=R(n,t.conv.bias),no(n)})}function pn(e,t){return k(()=>{let n=Pe(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Xn(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=R(n,t.bias),no(n)})}function RT(e,t){const n=Qr(e,t);function r(a,s){const c=ce(e(a)),p=ce(e(a));return t.push({paramPath:`${s}/sub`},{paramPath:`${s}/truediv`}),{sub:c,truediv:p}}function o(a,s,c){const p=n(a,s,3,`${c}/conv`),l=r(s,`${c}/bn`);return{conv:p,bn:l}}const i=eo(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:o,extractSeparableConvParams:i}}function $b(e,t,n,r){const{extractWeights:o,getRemainingWeights:i}=Ze(e),a=[],{extractConvParams:s,extractConvWithBatchNormParams:c,extractSeparableConvParams:p}=RT(o,a);let l;if(t.withSeparableConvs){const[h,d,b,x,w,L,S,I,N]=r,A=t.isFirstLayerConv2d?s(h,d,3,"conv0"):p(h,d,"conv0"),E=p(d,b,"conv1"),M=p(b,x,"conv2"),D=p(x,w,"conv3"),$=p(w,L,"conv4"),B=p(L,S,"conv5"),j=I?p(S,I,"conv6"):void 0,te=N?p(I,N,"conv7"):void 0,re=s(N||I||S,5*n,1,"conv8");l={conv0:A,conv1:E,conv2:M,conv3:D,conv4:$,conv5:B,conv6:j,conv7:te,conv8:re}}else{const[h,d,b,x,w,L,S,I,N]=r,A=c(h,d,"conv0"),E=c(d,b,"conv1"),M=c(b,x,"conv2"),D=c(x,w,"conv3"),$=c(w,L,"conv4"),B=c(L,S,"conv5"),j=c(S,I,"conv6"),te=c(I,N,"conv7"),re=s(N,5*n,1,"conv8");l={conv0:A,conv1:E,conv2:M,conv3:D,conv4:$,conv5:B,conv6:j,conv7:te,conv8:re}}if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{params:l,paramMappings:a}}function _T(e,t){const n=it(e,t);function r(s){const c=n(`${s}/sub`,1),p=n(`${s}/truediv`,1);return{sub:c,truediv:p}}function o(s){const c=n(`${s}/filters`,4),p=n(`${s}/bias`,1);return{filters:c,bias:p}}function i(s){const c=o(`${s}/conv`),p=r(`${s}/bn`);return{conv:c,bn:p}}const a=to(n);return{extractConvParams:o,extractConvWithBatchNormParams:i,extractSeparableConvParams:a}}function jb(e,t){const n=[],{extractConvParams:r,extractConvWithBatchNormParams:o,extractSeparableConvParams:i}=_T(e,n);let a;if(t.withSeparableConvs){const s=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:s>7?i("conv6"):void 0,conv7:s>8?i("conv7"):void 0,conv8:r("conv8")}}else a={conv0:o("conv0"),conv1:o("conv1"),conv2:o("conv2"),conv3:o("conv3"),conv4:o("conv4"),conv5:o("conv5"),conv6:o("conv6"),conv7:o("conv7"),conv8:r("conv8")};return Xe(e,n),{params:a,paramMappings:n}}var Zs;(function(e){e[e.XS=224]="XS",e[e.SM=320]="SM",e[e.MD=416]="MD",e[e.LG=608]="LG"})(Zs||(Zs={}));class Wt{constructor({inputSize:e,scoreThreshold:t}={}){if(this._name="TinyYolov2Options",this._inputSize=e||416,this._scoreThreshold=t||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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}}class ro extends Ue{constructor(e){super("TinyYolov2");Xs(e),this._config=e}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(e,t){let n=cn(e,t.conv0);return n=Ae(n,[2,2],[2,2],"same"),n=cn(n,t.conv1),n=Ae(n,[2,2],[2,2],"same"),n=cn(n,t.conv2),n=Ae(n,[2,2],[2,2],"same"),n=cn(n,t.conv3),n=Ae(n,[2,2],[2,2],"same"),n=cn(n,t.conv4),n=Ae(n,[2,2],[2,2],"same"),n=cn(n,t.conv5),n=Ae(n,[2,2],[1,1],"same"),n=cn(n,t.conv6),n=cn(n,t.conv7),fr(n,t.conv8,"valid",!1)}runMobilenet(e,t){let n=this.config.isFirstLayerConv2d?no(fr(e,t.conv0,"valid",!1)):pn(e,t.conv0);return n=Ae(n,[2,2],[2,2],"same"),n=pn(n,t.conv1),n=Ae(n,[2,2],[2,2],"same"),n=pn(n,t.conv2),n=Ae(n,[2,2],[2,2],"same"),n=pn(n,t.conv3),n=Ae(n,[2,2],[2,2],"same"),n=pn(n,t.conv4),n=Ae(n,[2,2],[2,2],"same"),n=pn(n,t.conv5),n=Ae(n,[2,2],[1,1],"same"),n=t.conv6?pn(n,t.conv6):n,n=t.conv7?pn(n,t.conv7):n,fr(n,t.conv8,"valid",!1)}forwardInput(e,t){const{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return k(()=>{let r=e.toBatchTensor(t,!1).toFloat();return r=this.config.meanRgb?wt(r,this.config.meanRgb):r,r=r.div(_(256)),this.config.withSeparableConvs?this.runMobilenet(r,n):this.runTinyYolov2(r,n)})}async forward(e,t){return await this.forwardInput(await ue(e),t)}async detect(e,t={}){const{inputSize:n,scoreThreshold:r}=new Wt(t),o=await ue(e),i=await this.forwardInput(o,n),a=k(()=>Ie(i)[0].expandDims()),s={width:o.getInputWidth(0),height:o.getInputHeight(0)},c=await this.extractBoxes(a,o.getReshapedInputDimensions(0),r);i.dispose(),a.dispose();const p=c.map(w=>w.box),l=c.map(w=>w.score),h=c.map(w=>w.classScore),d=c.map(w=>this.config.classes[w.label]),b=ta(p.map(w=>w.rescale(n)),l,this.config.iouThreshold,!0),x=b.map(w=>new On(l[w],h[w],d[w],p[w],s));return x}getDefaultModelName(){return""}extractParamsFromWeigthMap(e){return jb(e,this.config)}extractParams(e){const t=this.config.filterSizes||ro.DEFAULT_FILTER_SIZES,n=t?t.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return $b(e,this.config,this.boxEncodingSize,t)}async extractBoxes(e,t,n){const{width:r,height:o}=t,i=Math.max(r,o),a=i/r,s=i/o,c=e.shape[1],p=this.config.anchors.length,[l,h,d]=k(()=>{const L=e.reshape([c,c,p,this.boxEncodingSize]),S=L.slice([0,0,0,0],[c,c,p,4]),I=L.slice([0,0,0,4],[c,c,p,1]),N=this.withClassScores?rn(L.slice([0,0,0,5],[c,c,p,this.config.classes.length]),3):_(0);return[S,I,N]}),b=[],x=await h.array(),w=await l.array();for(let L=0;L<c;L++)for(let S=0;S<c;S++)for(let I=0;I<p;I++){const N=Lr(x[L][S][I][0]);if(!n||N>n){const A=(S+Lr(w[L][S][I][0]))/c*a,E=(L+Lr(w[L][S][I][1]))/c*s,M=Math.exp(w[L][S][I][2])*this.config.anchors[I].x/c*a,D=Math.exp(w[L][S][I][3])*this.config.anchors[I].y/c*s,$=A-M/2,B=E-D/2,j={row:L,col:S,anchor:I},{classScore:te,label:re}=this.withClassScores?await this.extractPredictedClass(d,j):{classScore:1,label:0};b.push(zs({box:new Rr($,B,$+M,B+D),score:N,classScore:N*te,label:re},j))}}return l.dispose(),h.dispose(),d.dispose(),b}async extractPredictedClass(e,t){const{row:n,col:r,anchor:o}=t,i=await e.array();return Array(this.config.classes.length).fill(0).map((a,s)=>i[n][r][o][s]).map((a,s)=>({classScore:a,label:s})).reduce((a,s)=>a.classScore>s.classScore?a:s)}}ro.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class xr extends ro{constructor(e=!0){const t=Object.assign({},{withSeparableConvs:e,iouThreshold:Db,classes:["face"]},e?{anchors:Mb,meanRgb:Ub}:{anchors:Fb,withClassScores:!0});super(t)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(e,t){const n=await this.detect(e,t);return n.map(r=>new fe(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?Bb:Wb}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}function Fu(e,t=!0){const n=new xr(t);return n.extractWeights(e),n}class Qs extends Wt{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class xt{async then(e){return e(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}async function br(e,t,n,r,o=({alignedRect:i})=>i){const i=e.map(c=>dn(c)?o(c):c.detection),a=r||(t instanceof ee?await Cn(t,i):await _n(t,i)),s=await n(a);return a.forEach(c=>c instanceof ee&&c.dispose()),s}async function oo(e,t,n,r,o){return br([e],t,async i=>n(i[0]),r,o)}const Gb=.4,Pb=[new J(1.603231,2.094468),new J(6.041143,7.080126),new J(2.882459,3.518061),new J(4.266906,5.178857),new J(9.041765,10.66308)],qb=[117.001,114.697,97.404];class yr extends ro{constructor(){const e={withSeparableConvs:!0,iouThreshold:Gb,classes:["face"],anchors:Pb,meanRgb:qb,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(e)}get anchors(){return this.config.anchors}async locateFaces(e,t){const n=await this.detect(e,t);return n.map(r=>new fe(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}const oe={ssdMobilenetv1:new Nn(),tinyFaceDetector:new yr(),tinyYolov2:new xr(),faceLandmark68Net:new Sr(),faceLandmark68TinyNet:new ha(),faceRecognitionNet:new vr(),faceExpressionNet:new da(),ageGenderNet:new La()},vc=(e,t)=>oe.ssdMobilenetv1.locateFaces(e,t),ju=(e,t)=>oe.tinyFaceDetector.locateFaces(e,t),Gu=(e,t)=>oe.tinyYolov2.locateFaces(e,t),Sc=e=>oe.faceLandmark68Net.detectLandmarks(e),Pu=e=>oe.faceLandmark68TinyNet.detectLandmarks(e),qu=e=>oe.faceRecognitionNet.computeFaceDescriptor(e),Hu=e=>oe.faceExpressionNet.predictExpressions(e),zu=e=>oe.ageGenderNet.predictAgeAndGender(e),Ic=e=>oe.ssdMobilenetv1.load(e),Yu=e=>oe.tinyFaceDetector.load(e),Vu=e=>oe.tinyYolov2.load(e),Ku=e=>oe.faceLandmark68Net.load(e),Ju=e=>oe.faceLandmark68TinyNet.load(e),Xu=e=>oe.faceRecognitionNet.load(e),Zu=e=>oe.faceExpressionNet.load(e),Qu=e=>oe.ageGenderNet.load(e),ed=Ic,td=vc,nd=Sc;class Hb extends xt{constructor(e,t,n){super();this.parentTask=e,this.input=t,this.extractedFaces=n}}class Ps extends Hb{async run(){const e=await this.parentTask,t=await br(e,this.input,async n=>await Promise.all(n.map(r=>oe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return e.map((n,r)=>uo(n,t[r]))}withAgeAndGender(){return new js(this,this.input)}}class qs extends Hb{async run(){const e=await this.parentTask;if(!e)return;const t=await oo(e,this.input,n=>oe.faceExpressionNet.predictExpressions(n),this.extractedFaces);return uo(e,t)}withAgeAndGender(){return new Gs(this,this.input)}}class ao extends Ps{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new hn(this,this.input)}}class co extends qs{withAgeAndGender(){return new so(this,this.input)}withFaceDescriptor(){return new un(this,this.input)}}class zb extends xt{constructor(e,t,n){super();this.parentTask=e,this.input=t,this.extractedFaces=n}}class js extends zb{async run(){const e=await this.parentTask,t=await br(e,this.input,async n=>await Promise.all(n.map(r=>oe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return e.map((n,r)=>{const{age:o,gender:i,genderProbability:a}=t[r];return fo(ho(n,i,a),o)})}withFaceExpressions(){return new Ps(this,this.input)}}class Gs extends zb{async run(){const e=await this.parentTask;if(!e)return;const{age:t,gender:n,genderProbability:r}=await oo(e,this.input,o=>oe.ageGenderNet.predictAgeAndGender(o),this.extractedFaces);return fo(ho(e,n,r),t)}withFaceExpressions(){return new qs(this,this.input)}}class io extends js{withFaceExpressions(){return new ao(this,this.input)}withFaceDescriptors(){return new hn(this,this.input)}}class so extends Gs{withFaceExpressions(){return new co(this,this.input)}withFaceDescriptor(){return new un(this,this.input)}}class pa extends xt{constructor(e,t){super();this.parentTask=e,this.input=t}}class hn extends pa{async run(){const e=await this.parentTask,t=await br(e,this.input,n=>Promise.all(n.map(r=>oe.faceRecognitionNet.computeFaceDescriptor(r))),null,n=>n.landmarks.align(null,{useDlibAlignment:!0}));return t.map((n,r)=>mo(e[r],n))}withFaceExpressions(){return new ao(this,this.input)}withAgeAndGender(){return new io(this,this.input)}}class un extends pa{async run(){const e=await this.parentTask;if(!e)return;const t=await oo(e,this.input,n=>oe.faceRecognitionNet.computeFaceDescriptor(n),null,n=>n.landmarks.align(null,{useDlibAlignment:!0}));return mo(e,t)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new so(this,this.input)}}class sa extends xt{constructor(e,t,n){super();this.parentTask=e,this.input=t,this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?oe.faceLandmark68TinyNet:oe.faceLandmark68Net}}class aa extends sa{async run(){const e=await this.parentTask,t=e.map(o=>o.detection),n=this.input instanceof ee?await Cn(this.input,t):await _n(this.input,t),r=await Promise.all(n.map(o=>this.landmarkNet.detectLandmarks(o)));return n.forEach(o=>o instanceof ee&&o.dispose()),e.map((o,i)=>Rn(o,r[i]))}withFaceExpressions(){return new ao(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new hn(this,this.input)}}class ca extends sa{async run(){const e=await this.parentTask;if(!e)return;const{detection:t}=e,n=this.input instanceof ee?await Cn(this.input,[t]):await _n(this.input,[t]),r=await this.landmarkNet.detectLandmarks(n[0]);return n.forEach(o=>o instanceof ee&&o.dispose()),Rn(e,r)}withFaceExpressions(){return new co(this,this.input)}withAgeAndGender(){return new so(this,this.input)}withFaceDescriptor(){return new un(this,this.input)}}class oa extends xt{constructor(e,t=new bt()){super();this.input=e,this.options=t}}class lo extends oa{async run(){const{input:e,options:t}=this,n=t instanceof Qs?r=>oe.tinyFaceDetector.locateFaces(r,t):t instanceof bt?r=>oe.ssdMobilenetv1.locateFaces(r,t):t instanceof Wt?r=>oe.tinyYolov2.locateFaces(r,t):null;if(!n)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return n(e)}runAndExtendWithFaceDetections(){return new Promise(async e=>{const t=await this.run();return e(t.map(n=>mn({},n)))})}withFaceLandmarks(e=!1){return new aa(this.runAndExtendWithFaceDetections(),this.input,e)}withFaceExpressions(){return new Ps(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new js(this.runAndExtendWithFaceDetections(),this.input)}}class ia extends oa{async run(){const e=await new lo(this.input,this.options);let t=e[0];return e.forEach(n=>{n.score>t.score&&(t=n)}),t}runAndExtendWithFaceDetection(){return new Promise(async e=>{const t=await this.run();return e(t?mn({},t):void 0)})}withFaceLandmarks(e=!1){return new ca(this.runAndExtendWithFaceDetection(),this.input,e)}withFaceExpressions(){return new qs(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Gs(this.runAndExtendWithFaceDetection(),this.input)}}function rd(e,t=new bt()){return new ia(e,t)}function po(e,t=new bt()){return new lo(e,t)}async function Tc(e,t){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await po(e,new bt(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function od(e,t={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await po(e,new Wt(t)).withFaceLandmarks().withFaceDescriptors()}const id=Tc;function ma(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((o,i)=>o-r[i]).reduce((o,i)=>o+Math.pow(i,2),0))}class Ac{constructor(e,t=.6){this._distanceThreshold=t;const n=Array.isArray(e)?e:[e];if(!n.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1;const o=()=>`person ${r++}`;this._labeledDescriptors=n.map(i=>{if(i instanceof xn)return i;if(i instanceof Float32Array)return new xn(o(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new xn(o(),[i.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(e,t){return t.map(n=>ma(n,e)).reduce((n,r)=>n+r,0)/(t.length||1)}matchDescriptor(e){return this.labeledDescriptors.map(({descriptors:t,label:n})=>new wo(n,this.computeMeanDistance(e,t))).reduce((t,n)=>t.distance<n.distance?t:n)}findBestMatch(e){const t=this.matchDescriptor(e);return t.distance<this.distanceThreshold?t:new wo("unknown",t.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(e=>e.toJSON())}}static fromJSON(e){const t=e.labeledDescriptors.map(n=>xn.fromJSON(n));return new Ac(t,e.distanceThreshold)}}function Mu(e){const t=new yr();return t.extractWeights(e),t}function Lc(e,t){const{width:n,height:r}=new Qe(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(o=>Lc(o,{width:n,height:r}));if(dn(e)){const o=e.detection.forSize(n,r),i=e.unshiftedLandmarks.forSize(o.box.width,o.box.height);return Rn(mn(e,o),i)}return Nt(e)?mn(e,e.detection.forSize(n,r)):e instanceof st||e instanceof fe?e.forSize(n,r):e}return fd();})();
/**
 * @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. 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. 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. */
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