diff --git a/demo/browser.js b/demo/browser.js
index 934ffe7d..9f442b06 100644
--- a/demo/browser.js
+++ b/demo/browser.js
@@ -8,13 +8,19 @@ import GLBench from './gl-bench.js';
 const userConfig = {
   backend: 'webgl',
   async: false,
+  profile: false,
   warmup: 'full',
   videoOptimized: true,
   filter: { enabled: true },
-  face: { enabled: false, mesh: { enabled: false }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: false } },
+  face: { enabled: true,
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
   hand: { enabled: false },
   gesture: { enabled: false },
-  body: { enabled: true, modelPath: '../models/efficientpose.json' },
+  body: { enabled: false, modelPath: '../models/blazepose.json' },
   object: { enabled: false },
 };
 
diff --git a/dist/demo-browser-index.js b/dist/demo-browser-index.js
index 25b32f0e..c1f3c0d0 100644
--- a/dist/demo-browser-index.js
+++ b/dist/demo-browser-index.js
@@ -5,31 +5,31 @@
   author: <https://github.com/vladmandic>'
   */
 
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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,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(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),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 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=$f(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if($f(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=pd(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:S}=b;return this.makeTensorFromDataId(_,x,S)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:u,attrs:c}=e,h=$f(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=Sf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}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 a=e;n==="string"&&Ta(e[0])&&(a=e.map(o=>Wc(o)));let s=r.write(a,t,n),i=new He(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=h5(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new He(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new jc(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof jc||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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(){let 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;let 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(let r of 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Buffer shape=${this.shape}`;throw new Error(a)}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];let 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 Dr().makeTensor(this.values,this.shape,this.dtype)}},Dr=null,ll=null,Gk=null;function qk(e){Dr=e}function Xk(e){ll=e}function Kk(e){Gk=e}var je=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=zt(e),this.strides=ao(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return ll.buffer(this.shape,this.dtype,e)}bufferSync(){return ll.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return so(this.shape,e)}arraySync(){return so(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Dr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>md(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();let e=Dr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>md(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();let e=await Dr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Dr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ll.print(this,e)}clone(){return this.throwIfDisposed(),ll.clone(this)}toString(e=!1){let t=this.dataSync();return jk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ll.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Dr().makeVariable(this,e,t,n)}};Object.defineProperty(je,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return Sf("Tensor",()=>je)}Z();var Hc=class extends je{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(!ia(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Dr().disposeTensor(this),this.dataId=e.dataId,Dr().incRef(this,null)}dispose(){Dr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Hc,Symbol.hasInstance,{value:e=>e instanceof je&&e.assign!=null&&e.assign instanceof Function});var br={};Le(br,{assertTypesMatch:()=>k5,getTensorsInContainer:()=>Cf,isTensorInList:()=>Zk,makeTypesMatch:()=>It});var Rf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Rf||(Rf={}));var Mf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Mf||(Mf={}));var Ff;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Ff||(Ff={}));var $f;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})($f||($f={}));var Df;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Df||(Df={}));var Yk={float32:$f,int32:Mf,bool:Ff,complex64:Df};function or(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Yk[e][t]}function yd(e){return or(e,"int32")}function It(e,t){if(e.dtype===t.dtype)return[e,t];let n=or(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function k5(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Zk(e,t){return t.some(n=>n.id===e.id)}function Cf(e){let t=[],n=new Set;return I5(e,t,n),t}function I5(e,t,n){if(e==null)return;if(e instanceof je){t.push(e);return}if(!Jk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),I5(s,t,n))}}function Jk(e){return Array.isArray(e)||typeof e=="object"}function Of(e){return e.kernelName!=null}var S5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},jc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new S5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,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(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),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 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Of(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=pd(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:S}=b;return this.makeTensorFromDataId(_,x,S)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=Of(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=Tf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*bf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Hc||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*bf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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(){let 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;let 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(let r of 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+          with dtype ${s.dtype}. `)}),n.length===1)return zr(n[0]);let r=n,a={axis:t};return $.runKernel(mo,r,a)}var it=D({concat_:sS});function iS(e){let t={x:C(e,"x","sigmoid")};return $.runKernel(Js,t)}var On=D({sigmoid_:iS});function oS(e,t,n){let r=C(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return $.runKernel(Ko,a,s)}var Fe=D({slice_:oS});function lS(e){let t={x:C(e,"x","tanh")};return $.runKernel(ai,t)}var yl=D({tanh_:lS});function cS(e,t,n,r,a,s){let i=C(e,"forgetBias","basicLSTMCell"),o=C(t,"lstmKernel","basicLSTMCell"),l=C(n,"lstmBias","basicLSTMCell"),c=C(r,"data","basicLSTMCell"),u=C(a,"c","basicLSTMCell"),h=C(s,"h","basicLSTMCell"),d=it([c,h],1),p=Ke(d,o),f=ie(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Fe(f,[0,0],y),w=Fe(f,[0,A],y),b=Fe(f,[0,A*2],y),_=Fe(f,[0,A*3],y),x=ie(O(On(g),yl(w)),O(u,On(ie(i,b)))),S=O(yl(x),On(_));return[x,S]}var uS=D({basicLSTMCell_:cS});function hS(e,t,n){let r=C(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);M(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),M(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(vc,s,i)}var Jc=D({batchToSpaceND_:hS});function dS(e){let t;return e.rank===0||e.rank===1?t=j(e,[1,1,1,e.size]):e.rank===2?t=j(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=j(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function pS(e,t,n,r,a,s){s==null&&(s=.001);let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;r!=null&&(u=C(r,"offset","batchNorm")),M(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${c.rank}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var Ax=D({batchNorm2d_:fS});function mS(e,t,n,r,a,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;return r!=null&&(u=C(r,"offset","batchNorm")),M(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),M(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var yx=D({batchNorm3d_:mS});function AS(e,t,n,r,a,s){let i=C(e,"x","batchNorm"),o=C(t,"mean","batchNorm"),l=C(n,"variance","batchNorm"),c;a!=null&&(c=C(a,"scale","batchNorm"));let u;return r!=null&&(u=C(r,"offset","batchNorm")),M(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),M(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var gx=D({batchNorm4d_:AS});function yS(e,t,n){let r=C(e,"x","bincount"),a=C(t,"weights","bincount");M(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return $.runKernel(Lh,s,i)}var xx=D({bincount_:yS});function gS(e,t){let n=C(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=j(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return zr(n);let i={x:n},o={reps:s};return $.runKernel(Ma,i,o)}var Qc=D({broadcastTo_:gS});function xS(e){let t={x:C(e,"x","ceil")};return $.runKernel(xs,t)}var fm=D({ceil_:xS});function wS(e,t,n){let r=C(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Ra,a,s)}var In=D({clipByValue_:wS});function bS(e){return it(e,0)}var wx=D({concat1d_:bS});function _S(e,t){return it(e,t)}var gl=D({concat2d_:_S});function vS(e,t){return it(e,t)}var bx=D({concat3d_:vS});function kS(e,t){return it(e,t)}var _x=D({concat4d_:kS});function IS(e,t,n,r,a="NHWC",s=[1,1],i){let o=C(e,"x","conv2d"),l=C(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=j(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&M(Gt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];M(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),M(Lr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:c,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=$.runKernel(ws,d,p);return u?j(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var la=D({conv2d_:IS});function SS(e,t,n,r,a="NWC",s=1,i){let o=C(e,"x","conv1d"),l=C(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=j(o,[1,o.shape[0],o.shape[1]])),M(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&M(Gt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(Lr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),M(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=j(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=j(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=la(d,h,[1,n],r,"NHWC",[1,s],i);return u?j(p,[p.shape[2],p.shape[3]]):j(p,[p.shape[0],p.shape[2],p.shape[3]])}var Sd=D({conv1d_:SS});function NS(e,t,n,r,a,s="NHWC",i){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=j(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),M(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&M(Gt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=$.runKernel(bs,d,p);return c?j(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var mm=D({conv2DBackpropInput_:NS});function TS(e,t,n,r,a,s){let i=C(e,"x","conv2dTranspose"),o=C(t,"filter","conv2dTranspose");return mm(n,i,o,r,a,"NHWC",s)}var Nd=D({conv2dTranspose_:TS});function ES(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=C(e,"x","conv3d"),o=C(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=j(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),M(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),M(Lr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),M(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=$.runKernel(Ic,u,h);return c?j(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Am=D({conv3d_:ES});function CS(e,t,n,r,a){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=j(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],c=i.shape[4];M(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),M(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel(Uh,u,h);return o?j(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var vx=D({conv3DBackpropInput_:CS});function RS(e,t,n,r,a){let s=C(e,"x","conv3dTranspose"),i=C(t,"filter","conv3dTranspose");return vx(n,s,i,r,a)}var MS=D({conv3dTranspose_:RS});function FS(e){let t={x:C(e,"x","cos")};return $.runKernel(_s,t)}var eu=D({cos_:FS});function $S(e){let t={x:C(e,"x","cosh")};return $.runKernel(Ao,t)}var Td=D({cosh_:$S});function DS(e,t=0,n=!1,r=!1){let a={x:C(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(vs,a,s)}var Ed=D({cumsum_:DS});function OS(e,t,n,r=!1){let a=C(e,"x","denseBincount"),s=C(t,"weights","denseBincount");M(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),M(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return $.runKernel(Hh,i,o)}var kx=D({denseBincount_:OS});function zS(e,t,n="NHWC"){let r=C(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${a} and ${t}  for depthToSpace with input shape
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PF={kernelName:Hh,backendName:"cpu",kernelFunc:zF};function LF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r;Se([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),f=new Pt(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,w=n.data.get(a.dataId).values,[b,_,x]=h,S=n.data.get(s.dataId).values,[T,E,F]=d,{batchSize:P,filterHeight:W,filterWidth:V,inChannels:U,inHeight:H,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:se,strideWidth:te}=p,le=W-1-p.padInfo.top,Q=V-1-p.padInfo.left,pe=G/U;for(let ce=0;ce<P;++ce)for(let ye=0;ye<U;++ye)for(let me=0;me<H;++me){let Ne=me-le,Ce=Math.max(0,Math.ceil(Ne/se)),De=Math.min(ee,(W+Ne)/se);for(let Pe=0;Pe<X;++Pe){let Oe=Pe-Q,nt=Math.max(0,Math.ceil(Oe/te)),rt=Math.min(Y,(V+Oe)/te),lt=0;for(let Je=Ce;Je<De;++Je){let ft=Je*se-Ne;for(let je=nt;je<rt;++je){let 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a=r.map(o=>n.data.get(o.dataId).values),s=Ue(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var IM={kernelName:fs,backendName:"cpu",kernelFunc:kM};function SM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;Se(a,"all");let o=v.parseAxisParam(s,a.shape),l=o,c=R.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=hr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=R.computeOutAndReduceShapes(u.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let b=0;b<p;++b){let _=m[g+b];w=w&&_}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=xt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var NM={kernelName:Dh,backendName:"cpu",kernelFunc:SM};function TM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;Se(a,"any");let o=v.parseAxisParam(s,a.shape),l=o,c=R.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=hr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=R.computeOutAndReduceShapes(u.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let b=0;b<p;++b){let _=m[g+b];w=w||_}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=xt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var EM={kernelName:Oh,backendName:"cpu",kernelFunc:TM};function CM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;Se(a,"argMax");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=hr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(u),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let b=0;b<f;++b){let _=m[y+b];_>g&&(g=_,w=b)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var RM={kernelName:ms,backendName:"cpu",kernelFunc:CM};function MM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;Se(a,"argMin");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=hr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(u),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let b=0;b<f;++b){let _=m[y+b];_<g&&(g=_,w=b)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var FM={kernelName:bc,backendName:"cpu",kernelFunc:MM},$M=ot(co,e=>Math.asin(e)),DM={kernelName:co,backendName:"cpu",kernelFunc:$M},OM=ot(uo,e=>Math.asinh(e)),zM={kernelName:uo,backendName:"cpu",kernelFunc:OM},PM=ot(ho,e=>Math.atan(e)),LM={kernelName:ho,backendName:"cpu",kernelFunc:PM},WM=$t((e,t)=>Math.atan2(e,t)),BM=Xt(fo,WM),VM={kernelName:fo,backendName:"cpu",kernelFunc:BM},UM=ot(po,e=>Math.atanh(e)),HM={kernelName:po,backendName:"cpu",kernelFunc:UM};function rA(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,c=a.dilationWidth,u=a.effectiveFilterHeight,h=a.effectiveFilterWidth,d=a.padInfo.top,p=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ue(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],w=a.outShape[3];for(let b=0;b<a.batchSize;++b){let _=b*y,x=b*r[0];for(let S=0;S<a.inChannels;++S)for(let T=0;T<a.outHeight;++T){let E=T*i-d,F=Math.max(0,E),P=Math.min(a.inHeight,u+E),W=_+T*g;for(let V=0;V<a.outWidth;++V){let U=V*o-p,H=Math.max(0,U),X=Math.min(a.inWidth,h+U),G=f,ee=0,Y=0;for(let te=F;te<P;te+=l){let le=x+te*r[1];for(let Q=H;Q<X;Q+=c){let pe=le+Q*r[2],ce=e[pe+S];s==="max"&&ce>G?G=ce:s==="avg"&&(ee+=ce,Y++)}if(isNaN(G))break}let se=W+V*w+S;A[se]=s==="avg"?ee/Y:G}}}return m}function Vw(e,t,n,r,a=!1,s=!1){let i=Ue(r.outShape,"int32"),o=r.strideHeight,l=r.strideWidth,c=r.dilationHeight,u=r.dilationWidth,h=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,f=r.padInfo.left,m=Ue(t,n,e);for(let A=0;A<r.batchSize;++A)for(let y=0;y<r.inChannels;++y)for(let g=0;g<r.outHeight;++g){let w=g*o-p,b=w;for(;b<0;)b+=c;let _=Math.min(r.inHeight,h+w);for(let x=0;x<r.outWidth;++x){let S=x*l-f,T=S;for(;T<0;)T+=u;let E=Math.min(r.inWidth,d+S),F=Number.NEGATIVE_INFINITY,P=-1;for(let W=b;W<_;W+=c){let V=W-w;for(let U=T;U<E;U+=u){let H=U-S,X=m.get(A,W,U,y);X>F&&(F=X,a?P=s?((A*r.inHeight+W)*r.inWidth+U)*r.inChannels+y:(W*r.inWidth+U)*r.inChannels+y:P=V*d+H)}}i.set(P,A,g,x,y)}}return i}function Uw(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=Ue(a.outShape,n),b=w.values,_=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],x=a.outShape[2]*a.outShape[3]*a.outShape[4],S=a.outShape[3]*a.outShape[4],T=a.outShape[4];for(let E=0;E<a.batchSize;++E){let F=E*_,P=E*r[0];for(let W=0;W<a.inChannels;++W)for(let V=0;V<a.outDepth;++V){let U=V*i-m,H=U;for(;H<0;)H+=c;let X=Math.min(a.inDepth,d+U),G=F+V*x;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-A,se=Y;for(;se<0;)se+=u;let te=Math.min(a.inHeight,p+Y),le=G+ee*S;for(let Q=0;Q<a.outWidth;++Q){let pe=Q*l-y,ce=pe;for(;ce<0;)ce+=h;let ye=Math.min(a.inWidth,f+pe),me=le+Q*T,Ne=g,Ce=0,De=0;for(let Oe=H;Oe<X;Oe+=c){let nt=P+Oe*r[1];for(let rt=se;rt<te;rt+=u){let lt=nt+rt*r[2];for(let Je=ce;Je<ye;Je+=h){let ft=lt+Je*r[3],He=e[ft+W];if(s==="max"&&He>Ne?Ne=He:s==="avg"&&(Ce+=He,De++),isNaN(Ne))break}if(isNaN(Ne))break}if(isNaN(Ne))break}let Pe=me+W;b[Pe]=s==="avg"?Ce/De:Ne}}}}return w}function jM(e,t){let n=Ue(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,w=g;for(;w<0;)w+=i;let b=Math.min(t.inDepth,c+g);for(let _=0;_<t.outHeight;++_){let x=_*a-p,S=x;for(;S<0;)S+=o;let T=Math.min(t.inHeight,u+x);for(let E=0;E<t.outWidth;++E){let F=E*s-f,P=F;for(;P<0;)P+=l;let W=Math.min(t.inWidth,h+F),V=Number.NEGATIVE_INFINITY,U=-1;for(let H=w;H<b;H+=i){let X=H-g;for(let G=S;G<T;G+=o){let ee=G-x;for(let Y=P;Y<W;Y+=l){let se=Y-F,te=e.get(m,H,G,Y,A);te>=V&&(V=te,U=X*u*h+ee*u+se)}}}n.set(U,m,y,_,E,A)}}}return n}function GM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Se(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))h=jr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=rA(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var qM={kernelName:As,backendName:"cpu",kernelFunc:GM};function XM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;Se(a,"avgPool3d");let u=R.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=Uw(h,a.shape,a.dtype,v.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var KM={kernelName:_c,backendName:"cpu",kernelFunc:XM};function ZM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;Se([a,s],"avgPool3DGrad");let u=R.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,w=u.dilationWidth,b=u.effectiveFilterDepth,_=u.effectiveFilterHeight,x=u.effectiveFilterWidth,S=b-1-u.padInfo.front,T=x-1-u.padInfo.left,E=_-1-u.padInfo.top,F=Ue(s.shape,"float32"),P=1/(f*m*A),W=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let U=0;U<u.inChannels;++U)for(let H=0;H<u.inDepth;++H)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=H-S,Y=X-E,se=G-T,te=0;for(let le=0;le<b;le+=y){let Q=(ee+le)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let pe=0;pe<_;pe+=g){let ce=(Y+pe)/d;if(!(ce<0||ce>=u.outHeight||Math.floor(ce)!==ce))for(let ye=0;ye<x;ye+=w){let me=(se+ye)/p;me<0||me>=u.outWidth||Math.floor(me)!==me||(te+=W.get(V,Q,ce,me,U))}}}F.set(te*P,V,H,X,G,U)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var YM={kernelName:Ph,backendName:"cpu",kernelFunc:ZM};function JM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Se([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,w=g-1-u.padInfo.left,b=y-1-u.padInfo.top,_=Ue(i.shape,"float32"),x=1/(p*f),S=n.data.get(a.dataId).values,T=Ue(a.shape,"float32",S);for(let E=0;E<u.batchSize;++E)for(let F=0;F<u.inChannels;++F)for(let P=0;P<u.inHeight;++P)for(let W=0;W<u.inWidth;++W){let V=P-b,U=W-w,H=0;for(let X=0;X<y;X+=m){let G=(V+X)/h;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=A){let Y=(U+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(H+=T.get(E,G,Y,F))}}_.set(H*x,E,P,W,F)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var QM={kernelName:zh,backendName:"cpu",kernelFunc:JM};function eF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Se([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),A=f.length,y=p.length,g=d.length,w=h.length,b=0,_=0,x=0,S=0;for(let T=0;T<u.length;++T)m[T]=f[b++]+(u[T]-h[_++])*p[x++]/Math.sqrt(d[S++]+c),b>=A&&(b=0),_>=w&&(_=0),x>=y&&(x=0),S>=g&&(S=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var tF={kernelName:Es,backendName:"cpu",kernelFunc:eF};function nF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;Se([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=xt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=hr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=xt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=ki({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var rF={kernelName:vc,backendName:"cpu",kernelFunc:nF};function aF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Xm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var sF={kernelName:Lh,backendName:"cpu",kernelFunc:aF},iF=ot(Ra,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),oF={kernelName:Ra,backendName:"cpu",kernelFunc:iF},lF=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},cF={kernelName:kc,backendName:"cpu",kernelFunc:lF};function Rl(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var uF={kernelName:Qh,backendName:"cpu",kernelFunc:Rl};function Ml(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(i)===0)return 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AF={kernelName:bs,backendName:"cpu",kernelFunc:mF};function yF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;Se([a,s],"conv3d");let c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=c,y=A.front,g=A.left,w=A.top,b=new Pt(c.outShape,a.dtype),_=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,S=b.values,T=v.computeStrides(a.shape),E=v.computeStrides(s.shape);for(let F=0;F<c.batchSize;++F){let P=F*T[0],W=F*b.strides[0];for(let V=0;V<c.outDepth;++V){let U=W+V*b.strides[1],H=V*c.strideDepth-y;for(let X=0;X<u;++X){let G=H+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*E[0],Y=P+G*T[1];for(let se=0;se<c.outHeight;++se){let te=U+se*b.strides[2],le=se*c.strideHeight-w;for(let Q=0;Q<h;++Q){let pe=le+Q*f;if(pe<0||pe>=c.inHeight)continue;let ce=ee+Q*E[1],ye=Y+pe*T[2];for(let me=0;me<c.outWidth;++me){let Ne=te+me*c.outChannels,Ce=me*c.strideWidth-g;for(let 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RF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=Xm(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=hw(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var MF={kernelName:Hh,backendName:"cpu",kernelFunc:RF};function FF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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e=$e.getNumber("WEBGL_VERSION");return e===0?0:bb(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Gc.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>_b($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>vb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>kb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>$e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);$e.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});$e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Gc.isMobile()&&$e.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function mn(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
+`)[0]),console.log(`%c ${v.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
+`))}function ob(e){return pa(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function lb(e,t){if(ve(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function fp(e,t){if(ve(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function cb(e,t){let n=pa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ve(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function ub(e,t){let n=pa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ve(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),ve(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function oz(){return J().getNumber("WEBGL_VERSION")===2?1:4}function hb(e){return pa(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function db(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function pb(e){return pa(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function lA(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),ve(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),ve(e,()=>e.enableVertexAttribArray(o)),!0)}function fb(e,t,n){Sb(e,n),ve(e,()=>e.activeTexture(e.TEXTURE0+n)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function lz(e,t){Sb(e,t),ve(e,()=>e.activeTexture(e.TEXTURE0+t)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function mb(e,t,n){return pa(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Ab(e,t,n){return e.getUniformLocation(t,n)}function yb(e,t,n,r){ve(e,()=>fb(e,t,r)),ve(e,()=>e.uniform1i(n,r))}function cz(e){ve(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ve(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),ve(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function mp(e,t,n){ve(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),ve(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function cA(e,t){ve(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),ve(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Au(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+gb(e,t))}function gb(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function pa(e,t,n){let r=ve(e,()=>t());if(r==null)throw new Error(n);return r}function Sb(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function Ii(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function Si(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function Ap(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Ii(e),...Si(e)]),t}function xb(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?v.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let r=v.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=Ii(e),s=2,i=2;return e.length&&([s,i]=Si(e)),r=a*(s/2)*(i/2),v.sizeToSquarishShape(r).map(o=>o*2)}return v.sizeToSquarishShape(r)}function gp(e){return e%2==0}function yu(e,t){if(e=e.slice(-2),t=t.slice(-2),v.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||gp(n)&&gp(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&gp(e[0])&&gp(t[0])}var xp,wp;function wb(e){if(xp==null){let t=Gr(e);xp=t.getParameter(t.MAX_TEXTURE_SIZE)}return xp}function uz(){xp=null}function hz(){wp=null}function bb(e){if(wp==null){let t=Gr(e);wp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,wp)}function _b(e){if(e===0)return 0;let t,n=Gr(e);return Jn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Jn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Jn(e,t){return e.getExtension(t)!=null}function uA(e){try{if(Gr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function vb(e){if(e===0)return!1;let t=Gr(e);if(e===1){if(!Jn(t,"OES_texture_float"))return!1}else if(!Jn(t,"EXT_color_buffer_float"))return!1;return pA(t)}function kb(e){if(e===0)return!1;let t=Gr(e);if(e===1){if(!Jn(t,"OES_texture_float")||!Jn(t,"WEBGL_color_buffer_float"))return!1}else{if(Jn(t,"EXT_color_buffer_float"))return pA(t);let n="EXT_color_buffer_half_float";if(Jn(t,n)){let r=t.getExtension(n);return bz(t,r)}return!1}return pA(t)}function pA(e){let t=dA(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function bz(e,t){let n=dA(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function Ib(e){return e!==2?!1:Gr(e).fenceSync!=null}function Fl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var $e=J();$e.registerFlag("HAS_WEBGL",()=>$e.getNumber("WEBGL_VERSION")>0);$e.registerFlag("WEBGL_VERSION",()=>uA(2)?2:uA(1)?1:0);$e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);$e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>$e.get("WEBGL_VERSION")===2);$e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);$e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);$e.registerFlag("WEBGL_PACK",()=>$e.getBool("HAS_WEBGL"));$e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_CLIP",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);$e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_REDUCE",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_LAZILY_UNPACK",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_CONV_IM2COL",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>wb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>bb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=$e.getNumber("WEBGL_VERSION");return e===0?0:_b(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Gc.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>vb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>kb($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Ib($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>$e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);$e.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});$e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Gc.isMobile()&&$e.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function mn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -40,7 +40,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       }
 
       #define isnan(value) isnan_custom(value)
-    `,l="",u=`
+    `,l="",c=`
       #define round(value) newRound(value)
       int newRound(float value) {
         return int(floor(value + 0.5));
@@ -66,7 +66,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isinf(vec4 val) {
         return equal(abs(val), vec4(INFINITY));
       }
-    `,u=`
+    `,c=`
       int round(float value) {
         return int(floor(value + 0.5));
       }
@@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Ti(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function dA(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ti(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function fA(e){let t=v.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var Sb=`
+`}var Nb=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -117,7 +117,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,vz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gu.DENSE;let t=wu(e),n=mn();this.outputShape=e,this.userCode=`
+`,_z=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gu.DENSE;let t=wu(e),n=mn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${Ti(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -138,7 +138,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},kz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gu.DENSE;let t=wu(e),n=mn();this.outputShape=e,this.userCode=`
+    `}},vz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gu.DENSE;let t=wu(e),n=mn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${Ti(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},Iz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qn.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
-      ${Sb}
+    `}},kz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qn.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
+      ${Nb}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},Sz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Qn.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
-      ${Sb}
+    `}},Iz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Qn.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
+      ${Nb}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},Nz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=mn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${dA(e)}
+    `}},Sz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=mn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${fA(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -204,10 +204,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},Tz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=mn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=`
+    `}},Nz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=mn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
           localCoords = coords;
-          if(localCoords[2] + ${u} < ${e[2]}) {
-            localCoords[2] += ${u};
+          if(localCoords[2] + ${c} < ${e[2]}) {
+            localCoords[2] += ${c};
             if(localCoords[1] + ${l} < ${e[1]}) {
               localCoords[1] += ${l};
 
@@ -222,18 +222,18 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
               values = ${r.texture2D}(A, uv);
 
               if(offset == 0) {
-                result[${c}] = values[0];
+                result[${u}] = values[0];
               } else if(offset == 1) {
-                result[${c}] = values[1];
+                result[${u}] = values[1];
               } else if(offset == 2) {
-                result[${c}] = values[2];
+                result[${u}] = values[2];
               } else {
-                result[${c}] = values[3];
+                result[${u}] = values[3];
               }
             }
           }
         `}this.userCode=`
-      ${dA(e)}
+      ${fA(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = ${o};
       }
-    `}},Nb={};Le(Nb,{bindVertexProgramAttributeStreams:()=>Ob,createBufferFromOutputTexture:()=>Lb,createFloat16MatrixTexture:()=>Mb,createFloat16PackedMatrixTexture:()=>Db,createFloat32MatrixTexture:()=>Rb,createIndexBuffer:()=>Cb,createPackedMatrixTexture:()=>$b,createUnsignedBytesMatrixTexture:()=>Fb,createVertexBuffer:()=>Eb,createVertexShader:()=>Tb,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Bb,downloadFloat32MatrixFromBuffer:()=>Wb,downloadMatrixFromPackedOutputTexture:()=>Ub,downloadPackedMatrixFromBuffer:()=>Vb,getInternalFormatForFloat16MatrixTexture:()=>fA,getInternalFormatForFloat16PackedMatrixTexture:()=>yA,getInternalFormatForFloat32MatrixTexture:()=>pA,getInternalFormatForPackedMatrixTexture:()=>AA,getInternalFormatForUnsignedBytesMatrixTexture:()=>mA,uploadDenseMatrixToTexture:()=>zb,uploadPixelDataToTexture:()=>Pb});function Tb(e){let t=mn(),n=`${t.version}
+    `}},Tb={};Le(Tb,{bindVertexProgramAttributeStreams:()=>zb,createBufferFromOutputTexture:()=>Wb,createFloat16MatrixTexture:()=>Fb,createFloat16PackedMatrixTexture:()=>Ob,createFloat32MatrixTexture:()=>Mb,createIndexBuffer:()=>Rb,createPackedMatrixTexture:()=>Db,createUnsignedBytesMatrixTexture:()=>$b,createVertexBuffer:()=>Cb,createVertexShader:()=>Eb,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Vb,downloadFloat32MatrixFromBuffer:()=>Bb,downloadMatrixFromPackedOutputTexture:()=>Hb,downloadPackedMatrixFromBuffer:()=>Ub,getInternalFormatForFloat16MatrixTexture:()=>AA,getInternalFormatForFloat16PackedMatrixTexture:()=>xA,getInternalFormatForFloat32MatrixTexture:()=>mA,getInternalFormatForPackedMatrixTexture:()=>gA,getInternalFormatForUnsignedBytesMatrixTexture:()=>yA,uploadDenseMatrixToTexture:()=>Pb,uploadPixelDataToTexture:()=>Lb});function Eb(e){let t=mn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return ab(e,n)}function Eb(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return lb(e,t)}function Cb(e){let t=new Uint16Array([0,1,2,2,1,3]);return cb(e,t)}function bu(e,t,n,r,a,s){hb(t,n);let i=ub(e),o=e.TEXTURE_2D;return ve(e,()=>e.bindTexture(o,i)),ve(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ve(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ve(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ve(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),ve(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function pA(e){return e.internalFormatFloat}function Rb(e,t,n,r){let[a,s]=xu(t,n);return bu(e,a,s,pA(r),r.textureFormatFloat,e.FLOAT)}function fA(e){return e.internalFormatHalfFloat}function Mb(e,t,n,r){let[a,s]=xu(t,n);return bu(e,a,s,fA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function mA(e){return e.downloadTextureFormat}function Fb(e,t,n,r){let[a,s]=xu(t,n);return bu(e,a,s,mA(r),e.RGBA,e.UNSIGNED_BYTE)}function AA(e){return e.internalFormatPackedFloat}function $b(e,t,n,r){let[a,s]=$l(t,n);return bu(e,a,s,AA(r),e.RGBA,e.FLOAT)}function yA(e){return e.internalFormatPackedHalfFloat}function Db(e,t,n,r){let[a,s]=$l(t,n);return bu(e,a,s,yA(r),e.RGBA,r.textureTypeHalfFloat)}function Ob(e,t,n){let r=0,a=3*4,s=3*4+2*4;return ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),iA(e,t,"clipSpacePos",n,3,s,r)&&iA(e,t,"uv",n,2,s,a)}function zb(e,t,n,r,a,s){ve(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Pb(e,t,n){ve(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Lb(e,t,n,r){let a=e.createBuffer();ve(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return ve(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ve(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ve(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Wb(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function Bb(e,t,n,r){let[a,s]=xu(t,n),i=4,o=new Uint8Array(mz(t*n,i));return ve(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Vb(e,t,n,r,a,s,i,o){let l=e,u=new Float32Array(Az(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Ub(e,t,n){let r=new Float32Array(t*n*4);return ve(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var bp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,yp(t,e)):this.gl=Gr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=mu(this.gl,a),Jn(this.gl,s))this.textureHalfFloatExtension=mu(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Jn(this.gl,r))this.colorBufferHalfFloatExtension=mu(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Jn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Jn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Eb(this.gl),this.indexBuffer=Cb(this.gl),this.framebuffer=db(this.gl),this.textureConfig=uA(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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+    }`;return sb(e,n)}function Cb(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return cb(e,t)}function Rb(e){let t=new Uint16Array([0,1,2,2,1,3]);return ub(e,t)}function bu(e,t,n,r,a,s){db(t,n);let i=hb(e),o=e.TEXTURE_2D;return ve(e,()=>e.bindTexture(o,i)),ve(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ve(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ve(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ve(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),ve(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function mA(e){return e.internalFormatFloat}function Mb(e,t,n,r){let[a,s]=xu(t,n);return bu(e,a,s,mA(r),r.textureFormatFloat,e.FLOAT)}function AA(e){return e.internalFormatHalfFloat}function Fb(e,t,n,r){let[a,s]=xu(t,n);return bu(e,a,s,AA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function yA(e){return e.downloadTextureFormat}function $b(e,t,n,r){let[a,s]=xu(t,n);return bu(e,a,s,yA(r),e.RGBA,e.UNSIGNED_BYTE)}function gA(e){return e.internalFormatPackedFloat}function Db(e,t,n,r){let[a,s]=$l(t,n);return bu(e,a,s,gA(r),e.RGBA,e.FLOAT)}function xA(e){return e.internalFormatPackedHalfFloat}function Ob(e,t,n,r){let[a,s]=$l(t,n);return bu(e,a,s,xA(r),e.RGBA,r.textureTypeHalfFloat)}function zb(e,t,n){let r=0,a=3*4,s=3*4+2*4;return ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),lA(e,t,"clipSpacePos",n,3,s,r)&&lA(e,t,"uv",n,2,s,a)}function Pb(e,t,n,r,a,s){ve(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Lb(e,t,n){ve(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Wb(e,t,n,r){let a=e.createBuffer();ve(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return ve(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ve(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ve(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Bb(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function Vb(e,t,n,r){let[a,s]=xu(t,n),i=4,o=new Uint8Array(fz(t*n,i));return ve(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Ub(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(mz(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function Hb(e,t,n){let r=new Float32Array(t*n*4);return ve(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var bp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,yp(t,e)):this.gl=Gr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=mu(this.gl,a),Jn(this.gl,s))this.textureHalfFloatExtension=mu(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Jn(this.gl,r))this.colorBufferHalfFloatExtension=mu(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Jn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Jn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Cb(this.gl),this.indexBuffer=Rb(this.gl),this.framebuffer=pb(this.gl),this.textureConfig=dA(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ve(e,()=>e.finish()),ve(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ve(e,()=>e.deleteFramebuffer(this.framebuffer)),ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ve(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ve(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Mb(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Fb(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),$b(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Lb(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Pb(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Ob(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Db(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(cA(this.gl,this.framebuffer),this.outputTexture=null),ve(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Vb(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Ub(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Bb(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=Wb(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Hb(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=ib(t,e),r=Eb(t),a=ob(t);return ve(t,()=>t.attachShader(a,r)),ve(t,()=>t.attachShader(a,n)),lb(t,a),this.debug&&fp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=zb(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ve(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&fp(this.gl,this.program),ve(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?mb(this.gl,e,t):Ab(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ve(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),yb(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=$l(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&fp(this.gl,this.program),Au(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ve(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ve(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=mu(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Tz(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mp(this.gl,e,this.framebuffer),this.debug&&Au(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mp(this.gl,this.outputTexture,this.framebuffer),this.debug&&Au(this.gl)):cA(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;mp(r,e,this.framebuffer),this.debug&&Au(r),this.outputTexture=e,ve(r,()=>r.viewport(0,0,t,n)),ve(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ve(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Tz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:jb}=R;function zz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
+`),i=e.map(p=>Ez(p,t,r)).join(`
+`),o=t.texShape,l=mn(),c=Mz(l),u,h,d=Dz(l);return t.isPacked?(u=Cz(t.logicalShape,o),h=$z(l)):(u=Rz(t.logicalShape,o),h=Fz(l)),r&&(d+=Oz),[d,c,h,s,u,i,n].join(`
+`)}function Dl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return Pz(e);case 1:return Lz(e);case 2:return Wz(e);case 3:return Bz(e);case 4:return Vz(e);case 5:return Uz(e);case 6:return Hz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Gb(e){switch(e.shapeInfo.logicalShape.length){case 0:return jz(e);case 1:return Gz(e);case 2:return qz(e);case 3:return Xz(e);default:return Kz(e)}}function Ez(e,t,n=!1){let r="";n?r+=Gb(e):r+=Dl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Zz(e,t):r+=Yz(e,t)),r}function Cz(e,t){switch(e.length){case 0:return qb();case 1:return Jz(e,t);case 2:return tP(e,t);case 3:return Qz(e,t);default:return eP(e,t)}}function Rz(e,t){switch(e.length){case 0:return qb();case 1:return nP(e,t);case 2:return oP(e,t);case 3:return rP(e,t);case 4:return aP(e,t);case 5:return sP(e,t);case 6:return iP(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Mz(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function $z(e){return`
+  `}function Fz(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function Dz(e){return`
+  `}function $z(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function Oz(e){return`${e.version}
+  `}function Dz(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
+    ${lP}
     ${cP}
     ${uP}
-    ${hP}
-  `}var cP=`
+  `}var lP=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,uP=`
+`,cP=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,hP=`
+`,uP=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,zz=`
+`,Oz=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function Gb(){return`
+`;function qb(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function Qz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function Jz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${n[1]}.0);
       }
@@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function rP(e,t){return t[0]===1?`
+  `}function nP(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function eP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
+  `}function Qz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function aP(e,t){let n=Ti(["r","c","d"],e);return`
+  `}function rP(e,t){let n=Ti(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function tP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function eP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function sP(e,t){let n=Ti(["r","c","d","d2"],e);return`
+  `}function aP(e,t){let n=Ti(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function iP(e,t){let n=Ti(["r","c","d","d2","d3"],e);return`
+  `}function sP(e,t){let n=Ti(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function oP(e,t){let n=Ti(["r","c","d","d2","d3","d4"],e);return`
+  `}function iP(e,t){let n=Ti(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -474,7 +474,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function nP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
+  `}function tP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -489,7 +489,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function lP(e,t){return v.arraysEqual(e,t)?`
+  `}function oP(e,t){return v.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -516,11 +516,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Ei(e){return`offset${e}`}function Gz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=mn();return`
+  `}function Ei(e){return`offset${e}`}function jz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=mn();return`
     vec4 ${n}() {
       return ${r.texture2D}(${t}, halfCR);
     }
-  `}function Lz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function Pz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
@@ -529,13 +529,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function qz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=mn();return`
+  `}function Gz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=mn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${a[0]}, ${a[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function Wz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function Lz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
         ${Ol(e)}
       }
@@ -558,18 +558,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function Xz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=mn();if(a!=null&&v.arraysEqual(t,a))return`
+  `}function qz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=mn();if(a!=null&&v.arraysEqual(t,a))return`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
         return ${o.texture2D}(${n}, uv);
       }
-    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(t[1]/2);return`
+    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
     vec4 ${r}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
+      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function Bz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
+  `}function Wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
     float ${r}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
       return sampleTexture(${n}, uv);
@@ -584,37 +584,37 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
         ${Ol(e)}
       }
-    `;let l=a[0],u=a[1],c=Ei(n);return u===1?`
+    `;let l=a[0],c=a[1],u=Ei(n);return c===1?`
     float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
+      float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
       return sampleTexture(${n}, uv);
     }
   `:l===1?`
     float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
+      float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
       return sampleTexture(${n}, uv);
     }
   `:`
   float ${r}(int row, int col) {
     // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${t[1]} + col + ${c};
-    vec2 uv = uvFromFlat(${l}, ${u}, index);
+    int index = row * ${t[1]} + col + ${u};
+    vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${n}, uv);
   }
-`}function Kz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=zl(e,h),f=["b","row","col"];return`
-        ${Hb(p)}
+`}function Xz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=zl(e,h),f=["b","row","col"];return`
+        ${Gb(p)}
         vec4 ${r}(int b, int row, int col) {
           return ${r}(${Pl(f,d)});
         }
-      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=mn();return`
+      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=mn();return`
     vec4 ${r}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
-        ${i}, ${o}, ${u}, ${l}, b, row, col);
-      return ${c.texture2D}(${n}, uv);
+        ${i}, ${o}, ${c}, ${l}, b, row, col);
+      return ${u.texture2D}(${n}, uv);
     }
-  `}function Vz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=zl(e,l),m=["row","col","depth"];return`
+  `}function Bz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=zl(e,l),m=["row","col","depth"];return`
         ${Dl(f)}
         float ${r}(int row, int col, int depth) {
           return ${r}(${Pl(m,o)});
@@ -625,29 +625,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                           vec3(${a}, ${s}, 1)));
         ${Ol(e)}
       }
-    `;let u=e.shapeInfo.texShape,c=u[0],h=u[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
+    `;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
         float ${r}(int row, int col, int depth) {
           float texR = float(row);
           float texC = dot(vec2(col, depth), vec2(${s}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${h}.0, ${c}.0);
+                     vec2(${h}.0, ${u}.0);
           return sampleTexture(${n}, uv);
         }
       `;if(h===s&&d==null)return`
     float ${r}(int row, int col, int depth) {
       float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
       float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${c}.0);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
       return sampleTexture(${n}, uv);
     }
   `;let p=Ei(n);return`
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${a} + col * ${s} + depth + ${p};
-        vec2 uv = uvFromFlat(${c}, ${h}, index);
+        vec2 uv = uvFromFlat(${u}, ${h}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function Zz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,c*=t[n-f-1],d=`b${f} * ${c} + `+d;let p=mn();return`
+  `}function Kz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=mn();return`
     vec4 ${a}(${h}) {
       int index = ${d};
       int texR = index / ${l};
@@ -655,7 +655,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${p.texture2D}(${r}, uv);
     }
-  `}function Uz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=zl(e,o),m=["row","col","depth","depth2"];return`
+  `}function Vz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=zl(e,o),m=["row","col","depth","depth2"];return`
       ${Dl(f)}
       float ${r}(int row, int col, int depth, int depth2) {
         return ${r}(${Pl(m,l)});
@@ -666,7 +666,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                           vec4(${i}, ${s}, ${a}, 1)));
         ${Ol(e)}
       }
-    `;let u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],d=c[1];if(d===i&&u==null)return`
+    `;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
         float texR = float(row);
         float texC =
@@ -676,7 +676,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                    vec2(${d}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(d===a&&u==null)return`
+    `;if(d===a&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
         float texR = dot(vec3(row, col, depth),
                          vec3(${t[1]*t[2]}, ${t[2]}, 1));
@@ -693,10 +693,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
       return sampleTexture(${n}, uv);
     }
-  `}function jz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=zl(e,l),A=["row","col","depth","depth2","depth3"];return`
+  `}function Uz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=zl(e,l),A=["row","col","depth","depth2","depth3"];return`
       ${Dl(m)}
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${Pl(A,u)});
+        return ${r}(${Pl(A,c)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -706,7 +706,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           depth3;
         ${Ol(e)}
       }
-    `;let c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&c==null)return`
+    `;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
@@ -715,7 +715,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                    vec2(${p}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(p===a&&c==null)return`
+    `;if(p===a&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
         float texR = dot(
           vec4(row, col, depth, depth2),
@@ -740,23 +740,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                     int depth2, int depth3, int depth4) {
         return ${r}(${Pl(y,s)});
       }
-    `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
+    `}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${c}, ${u}, ${l}, ${o})) +
+          vec4(${u}, ${c}, ${l}, ${o})) +
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
         ${Ol(e)}
       }
-    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===c&&h==null)return`
+    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${u}, ${l}, ${o}, ${i})) +
+          vec4(${c}, ${l}, ${o}, ${i})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${f}.0, ${p}.0);
@@ -779,7 +779,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${c} + col * ${u} + depth * ${l} +
+      int index = row * ${u} + col * ${c} + depth * ${l} +
           depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
       vec2 uv = uvFromFlat(${p}, ${f}, index);
       return sampleTexture(${n}, uv);
@@ -790,8 +790,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function Yz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=jb(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(`
-`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
+  `}function Zz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=jb(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
+`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?p=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
@@ -800,26 +800,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       `;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
     vec4 ${a}() {
       ${l} coords = getOutputCoords();
-      ${c}
+      ${u}
       vec4 outputValue = get${r}(${d});
       ${p}
     }
-  `}function Jz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
+  `}function Yz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let u=ut(l),c=jb(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${p[m+h]} = 0;`).join(`
+    `;let c=ut(l),u=jb(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
 `);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
     float ${a}() {
-      ${u} coords = getOutputCoords();
+      ${c} coords = getOutputCoords();
       ${d}
       return get${r}(${f});
     }
-  `}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function zl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Pl(e,t){return t.map(n=>e[n]).join(", ")}function dP(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=Pz(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(u,f,m),d[`offset${f}`]=e.getUniformLocation(u,`offset${f}`,m)}return{program:t,source:l,webGLProgram:u,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:c,nanLoc:h}}function qb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function pP(e,t,n,r,a){qb(t.inShapeInfos,n),qb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],c=t.uniformLocations[u],h=t.uniformLocations[`offset${u}`];if(c!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(c,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(c,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,c,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function fP(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:mP,bincountImpl:Xb,bincountReduceImpl:AP,ceilImpl:yP,concatImpl:gP,expImpl:xP,expm1Impl:wP,floorImpl:bP,gatherV2Impl:_P,greaterImpl:vP,lessImpl:kP,linSpaceImpl:IP,logImpl:SP,maxImpl:NP,maximumImpl:TP,minimumImpl:EP,multiplyImpl:CP,negImpl:RP,prodImpl:MP,rangeImpl:FP,rsqrtImpl:$P,simpleAbsImpl:Kb,sliceImpl:DP,stridedSliceImpl:OP,subImpl:zP,tileImpl:PP,topKImpl:LP,transposeImpl:gA,uniqueImpl:WP}=Hm;function Zb(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function An(e,t){return t===1?[e]:Zb(e,t)}function BP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var HP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
+  `}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function zl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Pl(e,t){return t.map(n=>e[n]).join(", ")}function hP(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=zz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function Xb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function dP(e,t,n,r,a){Xb(t.inShapeInfos,n),Xb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function pP(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:fP,bincountImpl:Kb,bincountReduceImpl:mP,ceilImpl:AP,concatImpl:yP,expImpl:gP,expm1Impl:xP,floorImpl:wP,gatherV2Impl:bP,greaterImpl:_P,lessImpl:vP,linSpaceImpl:kP,logImpl:IP,maxImpl:SP,maximumImpl:NP,minimumImpl:TP,multiplyImpl:EP,negImpl:CP,prodImpl:RP,rangeImpl:MP,rsqrtImpl:FP,simpleAbsImpl:Zb,sliceImpl:$P,stridedSliceImpl:DP,subImpl:OP,tileImpl:zP,topKImpl:PP,transposeImpl:wA,uniqueImpl:LP}=qm;function Yb(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function An(e,t){return t===1?[e]:Yb(e,t)}function WP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var HP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let n=An("rc",t),r=ut(t),a=VP(t,e,n),s=UP(t,e[e.length-1],e[e.length-2],n),i=jP(e,n);this.userCode=`
+      `;else{let n=An("rc",t),r=ut(t),a=BP(t,e,n),s=VP(t,e[e.length-1],e[e.length-2],n),i=UP(e,n);this.userCode=`
         void main() {
           ${r} rc = getOutputCoords();
 
@@ -831,7 +831,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function GP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function VP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function UP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
+      `}}};function jP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function BP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function VP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
     int r = ${a[0]};
     int c = ${a[1]};
     int rp1 = r + 1;
@@ -839,12 +839,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function jP(e,t){let n=e.length,r=GP(n,t);return n===1?`getA(rc),
+  `}function UP(e,t){let n=e.length,r=jP(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${r[0]}),
           cEdge ? 0. : getA(${r[1]}),
           rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}var Yb=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${r[3]})`}var Jb=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
         ${a}
         ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${r>0?"}":""}
       `}this.userCode=`
-      ${qP(t)}
-      ${dA(e)}
+      ${GP(t)}
+      ${fA(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -872,12 +872,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function qP(e){return`
+    `}};function GP(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
       ${Ti(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var XP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Qb(t,n),a=e_(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Jb(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===rn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===rn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===rn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===rn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===rn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Qb(n,r),s=e_(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Jb(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function KP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Jb(e,t,n,r,a){let s=ZP(t,r),i;if(a){let[l,u]=$l(e[0],e[1]);i=l*u}else{let[l,u]=xu(e[0],e[1]);i=l*u}let o=KP(n,s);return i*o}function ZP(e,t){switch(e){case rn.PACKED_2X2_FLOAT32:return AA(t);case rn.PACKED_2X2_FLOAT16:return yA(t);case rn.UNPACKED_FLOAT32:return pA(t);case rn.UNPACKED_FLOAT16:return fA(t);case rn.PACKED_4X1_UNSIGNED_BYTE:return mA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function YP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?rn.PACKED_2X2_FLOAT32:rn.UNPACKED_FLOAT32:e?rn.PACKED_2X2_FLOAT16:rn.UNPACKED_FLOAT16}function Qb(e,t){if(e===Qn.UPLOAD)return rn.PACKED_2X2_FLOAT32;if(e===Qn.RENDER||e==null)return YP(t);if(e===Qn.DOWNLOAD||e===Qn.PIXELS)return rn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function e_(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ga=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var qP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=e_(t,n),a=t_(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Qb(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===rn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===rn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===rn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===rn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===rn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=e_(n,r),s=t_(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Qb(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function XP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Qb(e,t,n,r,a){let s=KP(t,r),i;if(a){let[l,c]=$l(e[0],e[1]);i=l*c}else{let[l,c]=xu(e[0],e[1]);i=l*c}let o=XP(n,s);return i*o}function KP(e,t){switch(e){case rn.PACKED_2X2_FLOAT32:return gA(t);case rn.PACKED_2X2_FLOAT16:return xA(t);case rn.UNPACKED_FLOAT32:return mA(t);case rn.UNPACKED_FLOAT16:return AA(t);case rn.PACKED_4X1_UNSIGNED_BYTE:return yA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function ZP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?rn.PACKED_2X2_FLOAT32:rn.UNPACKED_FLOAT32:e?rn.PACKED_2X2_FLOAT16:rn.UNPACKED_FLOAT16}function e_(e,t){if(e===Qn.UPLOAD)return rn.PACKED_2X2_FLOAT32;if(e===Qn.RENDER||e==null)return ZP(t);if(e===Qn.DOWNLOAD||e===Qn.PIXELS)return rn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function t_(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ga=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},kr="if (isnan(x)) return x;",JP="return x;",t_="return abs(x);",QP="return (x >= 0.0) ? x : (exp(x) - 1.0);",eL=kr+`
+    `}},kr="if (isnan(x)) return x;",YP="return x;",n_="return abs(x);",JP="return (x >= 0.0) ? x : (exp(x) - 1.0);",QP=kr+`
   return (x < 0.0) ? 0.0 : x;
-`,tL=kr+`
+`,eL=kr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,_p="return x;",nL="return x;",rL=`
+`,_p="return x;",tL="return x;",nL=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,aL=`
+`,rL=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,sL=`
+`,aL=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -932,14 +932,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},iL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=An("rc",t),r=ut(t),a=BP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},sL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=An("rc",t),r=ut(t),a=WP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${r} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},oL=jr.whereImpl,lL=1e-7,cL=1e-4,xA={};function uL(e){return e in xA||(xA[e]={}),xA[e]}var hL=128,dL=600;function pL(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*dL/1024/1024}var Wl=class extends gc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Gr(J().getNumber("WEBGL_VERSION"));this.binaryCache=uL(J().getNumber("WEBGL_VERSION")),this.gpgpu=new bp(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new XP(this.gpgpu),this.numMBBeforeWarning=pL(),this.texData=new Rh(this,Pr())}nextDataId(){return Wl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Qn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Qn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Ll(i,_p):h=new Ga(i,_p);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=R.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Ll(r,_p):p=new Ga(r,_p);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...wu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Pr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!nb(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...wu(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?Ap(t):t,o=s?new Sz(i):new Iz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Pr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=hL){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return oL(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ll(e.shape,t),a=this.compileAndRun(r,[e],n);return Pr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Kb(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,t_,e.dtype);let t=new Ga(e.shape,t_),n=this.compileAndRun(t,[e]);return Pr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Pr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new iL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new HP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ii(e.shape),...Si(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Ii(t),...Si(t)],s=new Yb(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Ap(r),i;n?i=new kz(s):i=new vz(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===gu.DENSE){let m=wu(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!yu(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=fP(e,l,u),h=this.getAndSaveBinary(c,()=>dP(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),pP(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?lL:cL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=gb(n,o),t.texShape=c),a!=null){let h=Ap(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=$l(c[0],c[1]),d=new Tz(h,[f,p],m)):d=new Nz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Qn.PIXELS:this.texData.get(A.dataId).usage=Qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=fL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Wl.nextDataId=0;function fL(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var n_="3.3.0";function r_(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Gc.isBrowser()&&ml("webgl",()=>new Wl,2);var mL={forceHalfFloat:r_},a_=`
+    `}},iL=Hr.whereImpl,oL=1e-7,lL=1e-4,bA={};function cL(e){return e in bA||(bA[e]={}),bA[e]}var uL=128,hL=600;function dL(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*hL/1024/1024}var Wl=class extends gc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Gr(J().getNumber("WEBGL_VERSION"));this.binaryCache=cL(J().getNumber("WEBGL_VERSION")),this.gpgpu=new bp(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new qP(this.gpgpu),this.numMBBeforeWarning=dL(),this.texData=new Rh(this,Pr())}nextDataId(){return Wl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Qn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Qn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Ll(i,_p):h=new Ga(i,_p);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=R.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Ll(r,_p):p=new Ga(r,_p);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...wu(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Pr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!rb(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...wu(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?Ap(t):t,o=s?new Iz(i):new kz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Pr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=uL){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return iL(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ll(e.shape,t),a=this.compileAndRun(r,[e],n);return Pr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Zb(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,n_,e.dtype);let t=new Ga(e.shape,n_),n=this.compileAndRun(t,[e]);return Pr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Pr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new sL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new HP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ii(e.shape),...Si(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Ii(t),...Si(t)],s=new Jb(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Ap(r),i;n?i=new vz(s):i=new _z(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===gu.DENSE){let m=wu(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!yu(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=pP(e,l,c),h=this.getAndSaveBinary(u,()=>hP(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),dP(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oL:lL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=xb(n,o),t.texShape=u),a!=null){let h=Ap(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=$l(u[0],u[1]),d=new Nz(h,[f,p],m)):d=new Sz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Qn.PIXELS:this.texData.get(A.dataId).usage=Qn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=pL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Wl.nextDataId=0;function pL(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var r_="3.3.0";function a_(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Gc.isBrowser()&&ml("webgl",()=>new Wl,2);var fL={forceHalfFloat:a_},s_=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,Bl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
@@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Bn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var AL={kernelName:Rs,backendName:"webgl",kernelFunc:Bn};function qa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Bn({inputs:{x:r},backend:n}),l=Bn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var yL={kernelName:Wh,backendName:"webgl",kernelFunc:qa},s_="return (a < 0.) ? b * a : a;",i_=`
+    `}};function Bn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var mL={kernelName:Rs,backendName:"webgl",kernelFunc:Bn};function qa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Bn({inputs:{x:r},backend:n}),l=Bn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var AL={kernelName:Wh,backendName:"webgl",kernelFunc:qa},i_="return (a < 0.) ? b * a : a;",o_=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function gL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _u(i_,a.shape,i.shape):new Bl(s_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var xL={kernelName:Ms,backendName:"webgl",kernelFunc:gL},o_="return (a < 0.) ? b * a : a;",l_=`
+`;function yL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _u(o_,a.shape,i.shape):new Bl(i_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var gL={kernelName:Ms,backendName:"webgl",kernelFunc:yL},l_="return (a < 0.) ? b * a : a;",c_=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function wL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _u(l_,r.shape,a.shape):new Bl(o_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var bL={kernelName:js,backendName:"webgl",kernelFunc:wL},c_="if (isnan(x)) return x;",_L=`
+`;function xL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _u(c_,r.shape,a.shape):new Bl(l_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var wL={kernelName:Hs,backendName:"webgl",kernelFunc:xL},u_="if (isnan(x)) return x;",bL=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,vL=`
+`,_L=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Ll(i.shape,t):c=new Ga(i.shape,e),o.runWebGLProgram(c,[i],l)}}function an({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:_.dataId,dtype:_.dtype,shape:u.shape},T=new Bl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,S],or(b.dtype,_.dtype))}),g=qa({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||or(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),w=c.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new _u(t,l.shape,u.shape,n):p=new Bl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function kp(e,t=!1){if(e==="linear")return t?nL:JP;if(e==="relu")return t?aL:eL;if(e==="elu")return t?rL:QP;if(e==="relu6")return t?sL:tL;if(e==="prelu")return t?l_:o_;if(e==="leakyrelu")return t?i_:s_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var u_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Ll(i.shape,t):u=new Ga(i.shape,e),o.runWebGLProgram(u,[i],l)}}function an({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:_.dataId,dtype:_.dtype,shape:c.shape},T=new Bl(e,l.shape,c.shape);return u.runWebGLProgram(T,[x,S],or(b.dtype,_.dtype))}),g=qa({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||or(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new _u(t,l.shape,c.shape,n):p=new Bl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function kp(e,t=!1){if(e==="linear")return t?tL:YP;if(e==="relu")return t?rL:QP;if(e==="elu")return t?nL:JP;if(e==="relu6")return t?aL:eL;if(e==="prelu")return t?c_:l_;if(e==="leakyrelu")return t?o_:i_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var h_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1014,11 +1014,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${m}
 
-      const float sharedDimension = ${c}.0;
+      const float sharedDimension = ${u}.0;
 
       vec4 dot2x2ARowBCol(ivec3 rc) {
         vec4 result = vec4(0);
-        for (int i = 0; i < ${c}; i++) {
+        for (int i = 0; i < ${u}; i++) {
           int batchA = ${g};
           int batchB = ${w};
           vec4 a = getMatrixA(batchA, ${h});
@@ -1042,7 +1042,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},h_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},d_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},d_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},p_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},p_="return a * b;";function f_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new d_(h_.REAL,r.shape,a.shape),c=new d_(h_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=CP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new _u(p_,r.shape,a.shape):i=new Bl(p_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var kL={kernelName:Ws,backendName:"webgl",kernelFunc:f_};function IL(e,t,n){let r=[Ii(e.shape),...Si(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Ii(t),...Si(t)],i=new Yb(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function _e(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!yu(a.shape,l)&&!(c.texture!==null&&yu(c.shape,l))?IL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var SL={kernelName:Ho,backendName:"webgl",kernelFunc:_e},m_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=`
+    `}},f_="return a * b;";function m_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new p_(d_.REAL,r.shape,a.shape),u=new p_(d_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=EP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new _u(f_,r.shape,a.shape):i=new Bl(f_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var vL={kernelName:Ws,backendName:"webgl",kernelFunc:m_};function kL(e,t,n){let r=[Ii(e.shape),...Si(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Ii(t),...Si(t)],i=new Jb(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function _e(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),c=v.sizeFromShape(l);v.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!yu(a.shape,l)&&!(u.texture!==null&&yu(u.shape,l))?kL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var IL={kernelName:jo,backendName:"webgl",kernelFunc:_e},A_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1063,7 +1063,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${u}
+        ${c}
         return getX(batch, inIdx);
       }
 
@@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},NL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=`
+    `}},SL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1150,7 +1150,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float allValue = 1.0;
         float anyValue = 0.0;
 
-        for (int i = 0; i < ${u}; i += 4) {
+        for (int i = 0; i < ${c}; i += 4) {
           int inIdx = inOffset + i;
           ${d} values = ${d}(
             getValue(batch, inIdx),
@@ -1162,8 +1162,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${h}
         }
 
-        int inIdx = inOffset + ${u};
-        if (${c===1}) {
+        int inIdx = inOffset + ${c};
+        if (${u===1}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             initializationValue,
@@ -1172,7 +1172,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           );
 
           ${h}
-        } else if (${c===2}) {
+        } else if (${u===2}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1181,7 +1181,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           );
 
           ${h}
-        } else if (${c===3}) {
+        } else if (${u===3}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function TL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ci(e,t,n,r){let a=TL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],c,h;n==="mean"?c=i===0?new m_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new m_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):c=new NL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=s,s=r.runWebGLProgram(c,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var CL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=EL(t);this.userCode=`
+    `}};function NL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ci(e,t,n,r){let a=NL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new A_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new A_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new SL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var EL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=TL(t);this.userCode=`
     void main() {
       ${r} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function EL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var RL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ut(this.rank),a=Zb("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function TL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var CL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ut(this.rank),a=Yb("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${r} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1215,14 +1215,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Ip(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new RL(e.shape,t):new CL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function ML(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=Ip(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=_e({inputs:{x:c},attrs:{shape:[m,f]},backend:r}),y=yd(e.dtype),g=Ci(A,y,"sum",r),w=_e({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),w}function wA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return ML(a,s,i,n)}var FL={kernelName:ei,backendName:"webgl",kernelFunc:wA};function En(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c<l.length;c++)l[c]=a.shape[s[c]];let u;if(i.shouldExecuteOnCPU([a])){let c=i.texData.get(a.dataId).values,h=gA(c,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(u.dataId);d.values=h}else u=Ip(a,s,i);return u}var $L={kernelName:si,backendName:"webgl",kernelFunc:En},A_=1e3;function Sp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],S=_e({inputs:{x:e},backend:a,attrs:{shape:_}}),T=_e({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[S,T],F=Math.max(y,g),P=n?S.shape[1]:S.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",H=l!=null?kp(l,!0):null,X=W||V||U||H!=null,G;if((p===1||f===1)&&P>A_&&X===!1){let Y=S,se=T;n&&(Y=En({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(se=En({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(se));let te=f!==1,le=f===1,Q=Y;te&&(Q=_e({inputs:{x:Y},backend:a,attrs:{shape:[F,P,1]}}),E.push(Q));let pe=f===1?2:1,ce=se;le&&(ce=_e({inputs:{x:se},backend:a,attrs:{shape:[F,1,P]}}),E.push(ce));let ye=f_({inputs:{a:Q,b:ce},backend:a});G=wA({inputs:{x:ye},backend:a,attrs:{axis:pe,keepDims:!0}}),E.push(ye)}else{let Y=or(e.dtype,t.dtype),se=new u_(_,x,[F,p,f],n,r,W,H,V,U),te=[S,T];if(s!=null&&te.push(s),V&&te.push(i),U){let le=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(le),E.push(le)}G=a.runWebGLProgram(se,te,Y)}let ee=_e({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function DL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return Sp({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var OL={kernelName:ii,backendName:"webgl",kernelFunc:DL},y_="return abs(x);";function zL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Kb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,y_):a=new Ga(r.shape,y_),n.runWebGLProgram(a,[r],r.dtype)}var PL={kernelName:io,backendName:"webgl",kernelFunc:zL},LL=kr+`
+    `}};function Ip(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new CL(e.shape,t):new EL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function RL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=Ip(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=_e({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=yd(e.dtype),g=Ci(A,y,"sum",r),w=_e({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function _A(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return RL(a,s,i,n)}var ML={kernelName:ei,backendName:"webgl",kernelFunc:_A};function En(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=wA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=Ip(a,s,i);return c}var FL={kernelName:si,backendName:"webgl",kernelFunc:En},y_=1e3;function Sp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],S=_e({inputs:{x:e},backend:a,attrs:{shape:_}}),T=_e({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[S,T],F=Math.max(y,g),P=n?S.shape[1]:S.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",H=l!=null?kp(l,!0):null,X=W||V||U||H!=null,G;if((p===1||f===1)&&P>y_&&X===!1){let Y=S,se=T;n&&(Y=En({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(se=En({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(se));let te=f!==1,le=f===1,Q=Y;te&&(Q=_e({inputs:{x:Y},backend:a,attrs:{shape:[F,P,1]}}),E.push(Q));let pe=f===1?2:1,ce=se;le&&(ce=_e({inputs:{x:se},backend:a,attrs:{shape:[F,1,P]}}),E.push(ce));let ye=m_({inputs:{a:Q,b:ce},backend:a});G=_A({inputs:{x:ye},backend:a,attrs:{axis:pe,keepDims:!0}}),E.push(ye)}else{let Y=or(e.dtype,t.dtype),se=new h_(_,x,[F,p,f],n,r,W,H,V,U),te=[S,T];if(s!=null&&te.push(s),V&&te.push(i),U){let le=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(le),E.push(le)}G=a.runWebGLProgram(se,te,Y)}let ee=_e({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function $L(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return Sp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var DL={kernelName:ii,backendName:"webgl",kernelFunc:$L},g_="return abs(x);";function OL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Zb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,g_):a=new Ga(r.shape,g_),n.runWebGLProgram(a,[r],r.dtype)}var zL={kernelName:io,backendName:"webgl",kernelFunc:OL},PL=kr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,WL=Ye({opSnippet:LL}),BL={kernelName:oo,backendName:"webgl",kernelFunc:WL},VL=kr+`
+`,LL=Ye({opSnippet:PL}),WL={kernelName:oo,backendName:"webgl",kernelFunc:LL},BL=kr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,UL=Ye({opSnippet:VL}),jL={kernelName:lo,backendName:"webgl",kernelFunc:UL},g_="return a + b;",HL=an({opSnippet:g_,packedOpSnippet:g_,supportsComplex:!0,cpuKernelImpl:mP}),GL={kernelName:Ca,backendName:"webgl",kernelFunc:HL},qL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,VL=Ye({opSnippet:BL}),UL={kernelName:lo,backendName:"webgl",kernelFunc:VL},x_="return a + b;",HL=an({opSnippet:x_,packedOpSnippet:x_,supportsComplex:!0,cpuKernelImpl:fP}),jL={kernelName:Ca,backendName:"webgl",kernelFunc:HL},GL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,UL=Ye({opSnippet:VL}),jL={kernelName:lo,back
         float result = ${r};
         setOutput(result);
       }
-    `}},XL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},qL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,UL=Ye({opSnippet:VL}),jL={kernelName:lo,back
         vec4 result = ${r};
         setOutput(result);
       }
-    `}};function Np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Bn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Np({inputs:r.slice(0,o),backend:n}),u=Np({inputs:r.slice(o),backend:n});return Np({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>or(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new XL(r[0].shape,s):new qL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var KL={kernelName:fs,backendName:"webgl",kernelFunc:Np};function ZL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ci(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=_e({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=_e({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var YL={kernelName:Dh,backendName:"webgl",kernelFunc:ZL};function JL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ci(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=_e({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=_e({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var QL={kernelName:Oh,backendName:"webgl",kernelFunc:JL},eW=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function Np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Bn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Np({inputs:r.slice(0,o),backend:n}),c=Np({inputs:r.slice(o),backend:n});return Np({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>or(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new qL(r[0].shape,s):new GL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var XL={kernelName:fs,backendName:"webgl",kernelFunc:Np};function KL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("all",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ci(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=_e({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=_e({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var ZL={kernelName:Dh,backendName:"webgl",kernelFunc:KL};function YL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("any",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ci(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=_e({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=_e({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var JL={kernelName:Oh,backendName:"webgl",kernelFunc:YL},QL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1258,23 +1258,23 @@ return log(x + sqrt(x * x - 1.0));`,UL=Ye({opSnippet:VL}),jL={kernelName:lo,back
         }
         setOutput(float(bestIndex));
       }
-    `}},tW=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),u=An("coords",o),c,h;if(s===1){h=o+1;let S=ut(h);c=`
-        ${S} sourceLocR = ${S}(${u.join()}, 0);
-        ++${u[o-1]};
-        ${S} sourceLocG = ${S}(${u.join()}, 0);
-        ++${u[o-2]};
-        ${S} sourceLocA = ${S}(${u.join()}, 0);
-        --${u[o-1]};
-        ${S} sourceLocB = ${S}(${u.join()}, 0);
-        --${u[o-2]};`}else h=o,c=`
+    `}},eW=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),c=An("coords",o),u,h;if(s===1){h=o+1;let S=ut(h);u=`
+        ${S} sourceLocR = ${S}(${c.join()}, 0);
+        ++${c[o-1]};
+        ${S} sourceLocG = ${S}(${c.join()}, 0);
+        ++${c[o-2]};
+        ${S} sourceLocA = ${S}(${c.join()}, 0);
+        --${c[o-1]};
+        ${S} sourceLocB = ${S}(${c.join()}, 0);
+        --${c[o-2]};`}else h=o,u=`
         ${l} sourceLocR = coords;
-        ++${u[o-1]};
+        ++${c[o-1]};
         ${l} sourceLocG = coords;
-        ++${u[o-2]};
+        ++${c[o-2]};
         ${l} sourceLocA = coords;
-        --${u[o-1]};
+        --${c[o-1]};
         ${l} sourceLocB = coords;
-        --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(S=>"int "+S),m=An("sourceLocR",h-1).concat("inIdx.r"),A=An("sourceLocG",h-1).concat("inIdx.g"),y=An("sourceLocB",h-1).concat("inIdx.b"),g=An("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
+        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(S=>"int "+S),m=An("sourceLocR",h-1).concat("inIdx.r"),A=An("sourceLocG",h-1).concat("inIdx.g"),y=An("sourceLocB",h-1).concat("inIdx.b"),g=An("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                              getBestIndicesAChannel(${A.join()}),
                              getBestIndicesAChannel(${y.join()}),
@@ -1294,9 +1294,9 @@ return log(x + sqrt(x * x - 1.0));`,UL=Ye({opSnippet:VL}),jL={kernelName:lo,back
       ${x}
       void main() {
         ${l} coords = getOutputCoords();
-        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
-        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
-        ${c}
+        bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
+        bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
+        ${u}
         ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
           sourceLocB${p}, sourceLocA${p}) * ${t};
         ivec4 inIdx = srcIdx;
@@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,UL=Ye({opSnippet:VL}),jL={kernelName:lo,back
         }
         setOutput(bestIndex);
       }
-    `}};function x_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new eW(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=x_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function w_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new tW(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=w_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function b_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=_e({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=x_(e,u,r);s.push(c);let h=_e({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return w_(e,t,r)}function nW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=b_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var rW={kernelName:ms,backendName:"webgl",kernelFunc:nW};function aW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=b_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var sW={kernelName:bc,backendName:"webgl",kernelFunc:aW},iW=kr+`
+    `}};function w_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new QL(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=w_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function b_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new eW(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=b_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function __(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),c=_e({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=w_(e,c,r);s.push(u);let h=_e({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return b_(e,t,r)}function tW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=__(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var nW={kernelName:ms,backendName:"webgl",kernelFunc:tW};function rW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=__(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var aW={kernelName:bc,backendName:"webgl",kernelFunc:rW},sW=kr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,oW=Ye({opSnippet:iW}),lW={kernelName:co,backendName:"webgl",kernelFunc:oW},cW=kr+"return log(x + sqrt(x * x + 1.0));",uW=Ye({opSnippet:cW}),hW={kernelName:uo,backendName:"webgl",kernelFunc:uW},dW=kr+`
+`,iW=Ye({opSnippet:sW}),oW={kernelName:co,backendName:"webgl",kernelFunc:iW},lW=kr+"return log(x + sqrt(x * x + 1.0));",cW=Ye({opSnippet:lW}),uW={kernelName:uo,backendName:"webgl",kernelFunc:cW},hW=kr+`
   return atan(x);
-`,pW=Ye({opSnippet:dW}),fW={kernelName:ho,backendName:"webgl",kernelFunc:pW},mW=_L+`
+`,dW=Ye({opSnippet:hW}),pW={kernelName:ho,backendName:"webgl",kernelFunc:dW},fW=bL+`
   return atan(a, b);
-`,AW=`
+`,mW=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+vL+`
+  `+_L+`
   return result;
-`,yW=an({opSnippet:mW,packedOpSnippet:AW}),gW={kernelName:fo,backendName:"webgl",kernelFunc:yW},xW=kr+`
+`,AW=an({opSnippet:fW,packedOpSnippet:mW}),yW={kernelName:fo,backendName:"webgl",kernelFunc:AW},gW=kr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelName:po,backendName:"webgl",kernelFunc:wW},vu=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,xW=Ye({opSnippet:gW}),wW={kernelName:po,backendName:"webgl",kernelFunc:xW},vu=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${p});
 
@@ -1356,7 +1356,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           int minMaxPosition = 0;
           float avgValue = 0.0;
 
-          for (int wR = 0; wR < ${c};
+          for (int wR = 0; wR < ${u};
               wR += ${l}) {
             int xR = xRCorner + wR;
 
@@ -1365,7 +1365,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
             }
 
             for (int wC = 0; wC < ${h};
-                wC += ${u}) {
+                wC += ${c}) {
               int xC = xCCorner + wC;
 
               if (xC < 0 || xC >= ${e.inWidth}) {
@@ -1424,7 +1424,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wR = 0; wR < ${c};
+        for (int wR = 0; wR < ${u};
             wR += ${l}) {
           int xR = xRCorner + wR;
 
@@ -1433,13 +1433,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           }
 
           for (int wC = 0; wC < ${b}; wC += 4) {
-            int xC = xCCorner + wC * ${u};
+            int xC = xCCorner + wC * ${c};
 
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${u}, d),
-              getValue(batch, xR, xC + 2 * ${u}, d),
-              getValue(batch, xR, xC + 3 * ${u}, d)
+              getValue(batch, xR, xC + ${c}, d),
+              getValue(batch, xR, xC + 2 * ${c}, d),
+              getValue(batch, xR, xC + 3 * ${c}, d)
             );
 
             ${x}
@@ -1458,7 +1458,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           } else if (${_===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + ${c}, d),
               initializationValue,
               initializationValue
             );
@@ -1467,8 +1467,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           } else if (${_===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${u}, d),
-              getValue(batch, xR, xC + 2 * ${u}, d),
+              getValue(batch, xR, xC + ${c}, d),
+              getValue(batch, xR, xC + 2 * ${c}, d),
               initializationValue
             );
 
@@ -1477,7 +1477,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(${w});
       }
-    `}},bA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
+    `}},vA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${m}, ${A}, ${y});
@@ -1499,7 +1499,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           int minMaxPosition = 0;
 
           for (int wD = 0; wD < ${d};
-              wD += ${u}) {
+              wD += ${c}) {
             int xD = xDCorner + wD;
 
             if (xD < 0 || xD >= ${e.inDepth}) {
@@ -1507,7 +1507,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
             }
 
             for (int wR = 0; wR < ${p};
-                wR += ${c}) {
+                wR += ${u}) {
               int xR = xRCorner + wR;
 
               if (xR < 0 || xR >= ${e.inHeight}) {
@@ -1579,7 +1579,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         count = 0.0;
 
         for (int wD = 0; wD < ${d};
-            wD += ${u}) {
+            wD += ${c}) {
           int xD = xDCorner + wD;
 
           if (xD < 0 || xD >= ${e.inDepth}) {
@@ -1587,7 +1587,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           }
 
           for (int wR = 0; wR < ${p};
-            wR += ${c}) {
+            wR += ${u}) {
             int xR = xRCorner + wR;
 
             if (xR < 0 || xR >= ${e.inHeight}) {
@@ -1640,8 +1640,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           setOutput(${_});
         }
       }
-    `}};function _W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Bn({inputs:{x:a},backend:n});let h=new vu(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var vW={kernelName:As,backendName:"webgl",kernelFunc:_W};function kW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new bA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var IW={kernelName:_c,backendName:"webgl",kernelFunc:kW},SW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
-      const ivec2 pads = ivec2(${u}, ${c});
+    `}};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Bn({inputs:{x:a},backend:n});let h=new vu(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var _W={kernelName:As,backendName:"webgl",kernelFunc:bW};function vW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new vA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var kW={kernelName:_c,backendName:"webgl",kernelFunc:vW},IW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
+      const ivec2 pads = ivec2(${c}, ${u});
       const float avgMultiplier = float(${h});
 
       void main() {
@@ -1682,7 +1682,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},NW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
+    `}},SW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
       const ivec3 pads = ivec3(${p}, ${f}, ${m});
       const float avgMultiplier = float(${A});
 
@@ -1701,7 +1701,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${c};
+        for (int wD = 0; wD < ${u};
             wD += ${o}) {
           float dyD = float(dyDCorner + wD) / ${a}.0;
 
@@ -1721,7 +1721,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
             int idyR = int(dyR);
 
             for (int wC = 0; wC < ${d};
-                wC += ${u}) {
+                wC += ${c}) {
               float dyC = float(dyCCorner + wC) / ${i}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function TW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new NW(d);return n.runWebGLProgram(p,[a],i.dtype)}var EW={kernelName:Ph,backendName:"webgl",kernelFunc:TW};function CW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Fl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=new SW(c);return n.runWebGLProgram(h,[a],i.dtype)}var RW={kernelName:zh,backendName:"webgl",kernelFunc:CW};function MW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var FW={kernelName:ys,backendName:"webgl",kernelFunc:MW},$W=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function NW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new SW(d);return n.runWebGLProgram(p,[a],i.dtype)}var TW={kernelName:Ph,backendName:"webgl",kernelFunc:NW};function EW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Fl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=new IW(u);return n.runWebGLProgram(h,[a],i.dtype)}var CW={kernelName:zh,backendName:"webgl",kernelFunc:EW};function RW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var MW={kernelName:ys,backendName:"webgl",kernelFunc:RW},FW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},DW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},$W=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},OW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new DW(r.shape,a.shape,s.shape,c,h,l):new $W(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},zW={kernelName:Es,backendName:"webgl",kernelFunc:OW},LW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,r=PW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${_A[o]} = start[${o}] + coords.${_A[o]};`);a=`
+    `}},DW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new $W(r.shape,a.shape,s.shape,u,h,l):new FW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},OW={kernelName:Es,backendName:"webgl",kernelFunc:DW},PW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,r=zW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${kA[o]} = start[${o}] + coords.${kA[o]};`);a=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${a}
         setOutput(getSource(${r}));
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},_A=["x","y","z","w","u","v"];function PW(e){if(e===1)return"sourceLoc";if(e<=6)return _A.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var WW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=An("coords",this.rank),r=An("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},kA=["x","y","z","w","u","v"];function zW(e){if(e===1)return"sourceLoc";if(e<=6)return kA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var LW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=An("coords",this.rank),r=An("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${r[this.rank-1]};
@@ -1790,7 +1790,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
       }
     `,l=this.rank<=4?`sourceLoc = coords +
-            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
+            ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
 `);this.userCode=`
       uniform int start[${this.rank}];
       void main() {
@@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${o}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function BW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=dn.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function ku(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=dn.parseSliceParams(a,s,i);if(dn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=DP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=dn.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WW(l):new LW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),BW(a,o,l,n)}var VW={kernelName:Ko,backendName:"webgl",kernelFunc:ku},UW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=[],f=_e({inputs:{x:a},backend:n,attrs:{shape:l}}),m=En({inputs:{x:f},backend:n,attrs:{perm:u}}),A=_e({inputs:{x:m},backend:n,attrs:{shape:c}}),y=ku({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},jW={kernelName:vc,backendName:"webgl",kernelFunc:UW};function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Xb(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var GW={kernelName:Lh,backendName:"webgl",kernelFunc:HW},qW="return float(a != b);",__=an({opSnippet:qW,dtype:"bool"}),XW={kernelName:zo,backendName:"webgl",kernelFunc:__};function Iu(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Bn({inputs:{x:a.complexTensorInfos.real},backend:n})}var KW={kernelName:id,backendName:"webgl",kernelFunc:Iu},ZW="return float(int(x));";function YW(e,t){let n=new Ga(e.shape,ZW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function vA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Bn({inputs:{x:a},backend:n});let i=Ft(a.shape),o=vA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=qa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Iu({inputs:{input:a},backend:n}),o=vA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Bn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return YW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=__({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var JW={kernelName:gs,backendName:"webgl",kernelFunc:vA},v_="return ceil(x);",QW=Ye({opSnippet:v_,packedOpSnippet:v_,cpuKernelImpl:yP}),eB={kernelName:xs,backendName:"webgl",kernelFunc:QW},tB=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function WW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=dn.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function ku(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=dn.parseSliceParams(a,s,i);if(dn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=$P(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=dn.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LW(l):new PW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),WW(a,o,l,n)}var BW={kernelName:Ko,backendName:"webgl",kernelFunc:ku},VW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=[],f=_e({inputs:{x:a},backend:n,attrs:{shape:l}}),m=En({inputs:{x:f},backend:n,attrs:{perm:c}}),A=_e({inputs:{x:m},backend:n,attrs:{shape:u}}),y=ku({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},UW={kernelName:vc,backendName:"webgl",kernelFunc:VW};function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=Kb(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var jW={kernelName:Lh,backendName:"webgl",kernelFunc:HW},GW="return float(a != b);",v_=an({opSnippet:GW,dtype:"bool"}),qW={kernelName:zo,backendName:"webgl",kernelFunc:v_};function Iu(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Bn({inputs:{x:a.complexTensorInfos.real},backend:n})}var XW={kernelName:id,backendName:"webgl",kernelFunc:Iu},KW="return float(int(x));";function ZW(e,t){let n=new Ga(e.shape,KW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function IA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Bn({inputs:{x:a},backend:n});let i=Ft(a.shape),o=IA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=qa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Iu({inputs:{input:a},backend:n}),o=IA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Bn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return ZW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=v_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var YW={kernelName:gs,backendName:"webgl",kernelFunc:IA},k_="return ceil(x);",JW=Ye({opSnippet:k_,packedOpSnippet:k_,cpuKernelImpl:AP}),QW={kernelName:xs,backendName:"webgl",kernelFunc:JW},eB=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},nB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},tB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function rB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new nB(a.shape):o=new tB(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var aB={kernelName:Ra,backendName:"webgl",kernelFunc:rB},sB=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new tB(a.shape):o=new eB(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var rB={kernelName:Ra,backendName:"webgl",kernelFunc:nB},aB=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function k_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function iB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new sB(r.shape),i=[k_(r,a.complexTensorInfos.real),k_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var oB={kernelName:kc,backendName:"webgl",kernelFunc:iB},lB=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
+    `}};function I_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function sB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new aB(r.shape),i=[I_(r,a.complexTensorInfos.real),I_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var iB={kernelName:kc,backendName:"webgl",kernelFunc:sB},oB=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1851,18 +1851,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${n.join(`
         `)}
       }
-    `}},cB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ut(r),s=An("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),c=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},lB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ut(r),s=An("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
-            getT0(${c}), vec2(${u.join()}));
+            getT0(${u}), vec2(${c.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
             getT${f}(${Tp(i,l,m)}),
-            vec2(${Tp(u,l,m)}));
+            vec2(${Tp(c,l,m)}));
         }`}let d=o.length,p=o[o.length-1];h+=`
         return getChannel(
           getT${d}(${Tp(i,l,p)}),
-          vec2(${Tp(u,l,p)}));`,this.userCode=`
+          vec2(${Tp(c,l,p)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -1888,7 +1888,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(result);
       }
-    `}};function Tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Ep(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Bn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var uB={kernelName:Qh,backendName:"webgl",kernelFunc:Ep};function Vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>Iu({inputs:{input:f},backend:n})),c=e.map(f=>Ep({inputs:{input:f},backend:n})),h=Vl(u,t,n),d=Vl(c,t,n),p=qa({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=I_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=gP(h,c,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=Vl(e.slice(0,u),t,n),h=Vl(e.slice(u),t,n),d=Vl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new cB(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=I_(e,t,n),i=new lB(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=_e({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function I_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>_e({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function S_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Bn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),Vl(o,s,n)}var hB={kernelName:mo,backendName:"webgl",kernelFunc:S_},N_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
+    `}};function Tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Ep(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Bn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var cB={kernelName:Qh,backendName:"webgl",kernelFunc:Ep};function Vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>Iu({inputs:{input:f},backend:n})),u=e.map(f=>Ep({inputs:{input:f},backend:n})),h=Vl(c,t,n),d=Vl(u,t,n),p=qa({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=S_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=yP(h,u,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Vl(e.slice(0,c),t,n),h=Vl(e.slice(c),t,n),d=Vl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new lB(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=S_(e,t,n),i=new oB(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=_e({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function S_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>_e({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function N_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(c=>c.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>v.sizeFromShape(c.shape)>0);if(o.length===1)return Bn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return R.assertParamsConsistent(l,s),Vl(o,s,n)}var uB={kernelName:mo,backendName:"webgl",kernelFunc:N_},T_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:a?w=`float activation(float a) {
@@ -1918,14 +1918,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
         for (int wR = 0; wR < ${h}; wR++) {
-          int xR = xRCorner + wR * ${u};
+          int xR = xRCorner + wR * ${c};
 
           if (xR < 0 || xR >= ${e.inHeight}) {
             continue;
           }
 
           for (int wC = 0; wC < ${d}; wC++) {
-            int xC = xCCorner + wC * ${c};
+            int xC = xCCorner + wC * ${u};
 
             if (xC < 0 || xC >= ${e.inWidth}) {
               continue;
@@ -2022,7 +2022,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${b}
         setOutput(result);
       }
-    `}},dB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},hB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${r});
 
@@ -2040,7 +2040,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         // y(yF, yR, yC, d2). ? = to be determined. : = across all
         // values in that axis.
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${c}; wF++) {
+        for (int wF = 0; wF < ${u}; wF++) {
           int xF = xFCorner + wF * ${o};
 
           if (xF < 0 || xF >= ${e.inDepth}) {
@@ -2055,7 +2055,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
             }
 
             for (int wC = 0; wC < ${d}; wC++) {
-              int xC = xCCorner + wC * ${u};
+              int xC = xCCorner + wC * ${c};
 
               if (xC < 0 || xC >= ${e.inWidth}) {
                 continue;
@@ -2110,18 +2110,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},pB=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=mn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
+    `}},dB=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=mn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
           blockIndex = rc.y + ${_};
           pos = rc.x + ${b};
 
           if(blockIndex < ${e[1]} && pos < ${e[0]}) {
             offsetY = int(blockIndex / (${l})) * ${i} - ${p};
-            d0 = offsetY + ${c} * (pos / ${f});
+            d0 = offsetY + ${u} * (pos / ${f});
 
             if(d0 < ${t[y]} && d0 >= 0) {
 
               offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
-              d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.));
+              d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
 
               if(d1 < ${t[g]} && d1 >= 0) {
 
@@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
         ${m.output} = result;
       }
-    `}};function T_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>A_,w=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=_e({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=_e({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Sp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=_e({inputs:{x:S},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(S)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(yu(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let S=_e({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let T=Sp({a:_,b:S,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=Bn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function E_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=_e({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=_e({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let S=new pB(y,_.shape,n),T=r.runWebGLProgram(S,[_],"float32"),E=_e({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let F=a!=null,P=s!=null,W=o==="leakyrelu",V=o?kp(o,!0):null,U=new u_(E.shape,x.shape,[1,A,n.outChannels],g,w,F,V,P,W),H=[E,x];if(a&&H.push(a),P&&H.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=_e({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function fB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=T_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=E_({x:a,filter:s,convInfo:d,backend:n});else{let m=new N_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=_e({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var mB={kernelName:ws,backendName:"webgl",kernelFunc:fB},AB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function E_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>y_,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=_e({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=_e({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Sp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=_e({inputs:{x:S},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(S)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(yu(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let S=_e({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let T=Sp({a:_,b:S,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,E.shape=n.outShape,A=Bn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function C_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=_e({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=_e({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let S=new dB(y,_.shape,n),T=r.runWebGLProgram(S,[_],"float32"),E=_e({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let F=a!=null,P=s!=null,W=o==="leakyrelu",V=o?kp(o,!0):null,U=new h_(E.shape,x.shape,[1,A,n.outChannels],g,w,F,V,P,W),H=[E,x];if(a&&H.push(a),P&&H.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=_e({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function pB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=E_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=C_({x:a,filter:s,convInfo:d,backend:n});else{let m=new T_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=_e({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var fB={kernelName:ws,backendName:"webgl",kernelFunc:pB},mB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2196,15 +2196,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},yB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`
+    `}},AB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d1 = coords[${c}];
+        int d1 = coords[${u}];
 
-        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
+        ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
         int dyRCorner = dyCorner.x;
         int dyCCorner = dyCorner.y;
 
@@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},gB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},yB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2291,8 +2291,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},xB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${o}, ${l}, ${u});
+    `}},gB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${o}, ${l}, ${c});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function wB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new AB(d);return n.runWebGLProgram(p,[a,s],"float32")}var bB={kernelName:Bh,backendName:"webgl",kernelFunc:wB};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(u),d=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new yB(d);return n.runWebGLProgram(p,[a,s],"float32")}var vB={kernelName:bs,backendName:"webgl",kernelFunc:_B};function kB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new dB(u);return n.runWebGLProgram(c,[a,s],"float32")}var IB={kernelName:Ic,backendName:"webgl",kernelFunc:kB};function SB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=R.computeConv3DInfo(a.shape,l,i,1,o),c=new gB(u);return n.runWebGLProgram(c,[a,s],"float32")}var NB={kernelName:Vh,backendName:"webgl",kernelFunc:SB};function TB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=R.computeConv3DInfo(l,s.shape,o,1,i),c=new xB(u);return n.runWebGLProgram(c,[a,s],"float32")}var EB={kernelName:Uh,backendName:"webgl",kernelFunc:TB},CB=c_+`
+    `}};function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new mB(d);return n.runWebGLProgram(p,[a,s],"float32")}var wB={kernelName:Bh,backendName:"webgl",kernelFunc:xB};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(c),d=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new AB(d);return n.runWebGLProgram(p,[a,s],"float32")}var _B={kernelName:bs,backendName:"webgl",kernelFunc:bB};function vB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new hB(c);return n.runWebGLProgram(u,[a,s],"float32")}var kB={kernelName:Ic,backendName:"webgl",kernelFunc:vB};function IB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=R.computeConv3DInfo(a.shape,l,i,1,o),u=new yB(c);return n.runWebGLProgram(u,[a,s],"float32")}var SB={kernelName:Vh,backendName:"webgl",kernelFunc:IB};function NB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=R.computeConv3DInfo(l,s.shape,o,1,i),u=new gB(c);return n.runWebGLProgram(u,[a,s],"float32")}var TB={kernelName:Uh,backendName:"webgl",kernelFunc:NB},EB=u_+`
   return cos(x);
-`,RB=Ye({opSnippet:CB}),MB={kernelName:_s,backendName:"webgl",kernelFunc:RB},FB=`
+`,CB=Ye({opSnippet:EB}),RB={kernelName:_s,backendName:"webgl",kernelFunc:CB},MB=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,$B=Ye({opSnippet:FB}),DB={kernelName:Ao,backendName:"webgl",kernelFunc:$B},OB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,FB=Ye({opSnippet:MB}),$B={kernelName:Ao,backendName:"webgl",kernelFunc:FB},DB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${g});
       void main() {
@@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           setOutput(newValue);
         }
       }
-    `}},zB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new OB(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},PB={kernelName:yo,backendName:"webgl",kernelFunc:zB},M_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${C_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},OB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new DB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},zB={kernelName:yo,backendName:"webgl",kernelFunc:OB},F_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${R_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
         ${ut(r)} coords = getOutputCoords();
-        int end = ${R_(r,"coords")};
+        int end = ${M_(r,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${R_(r,"coords")} = idx;
-          val += getX(${C_(r,"coords")});
+          ${M_(r,"coords")} = idx;
+          val += getX(${R_(r,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function C_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function R_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=R.getAxesPermutation([s],l),c=a;u!=null&&(c=En({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Bn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new M_(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new M_(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=En({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var WB={kernelName:vs,backendName:"webgl",kernelFunc:LB};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Xb(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=AP(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var VB={kernelName:jh,backendName:"webgl",kernelFunc:BB},UB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function R_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function M_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=R.getAxesPermutation([s],l),u=a;c!=null&&(u=En({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Bn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new F_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new F_(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=R.getUndoAxesPermutation(c),m=En({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var LB={kernelName:vs,backendName:"webgl",kernelFunc:PB};function WB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=Kb(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=mP(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var BB={kernelName:Hh,backendName:"webgl",kernelFunc:WB},VB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2447,7 +2447,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function jB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new UB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var HB={kernelName:go,backendName:"webgl",kernelFunc:jB},F_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function UB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new VB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var HB={kernelName:go,backendName:"webgl",kernelFunc:UB},$_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:a?A=`float activation(float a) {
@@ -2460,7 +2460,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${A}
 
-      const ivec2 strides = ivec2(${u}, ${c});
+      const ivec2 strides = ivec2(${c}, ${u});
       const ivec2 pads = ivec2(${o}, ${l});
 
       void main() {
@@ -2503,13 +2503,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${y}
         setOutput(result);
       }
-    `}},$_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
+    `}},D_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
           vec4 xTexelR${b}C${_*2} = vec4(0.);
           vec4 wR${b}C${_} = vec4(0.);
           vec4 xR${b}C${_} = vec4(0.);`;for(let b=0;b<p;b++)for(let _=0;_<m;_++){let x=_*2;if(A+=`
           xR = xRCorner + ${b*h};
           xC = xCCorner + ${x*d};
-        `,c===1){if(x<f&&(l%2==1?A+=`
+        `,u===1){if(x<f&&(l%2==1?A+=`
                 xCOffset = xC + 1;
                 if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
@@ -2575,7 +2575,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
                 `}}else x<f&&(A+=`
               if(xR >= 0 && xR < ${s}) {
             `,l%2==1?(A+=`
-                xCOffset = xC + 1 - ${c};
+                xCOffset = xC + 1 - ${u};
                 if(xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
                 } else {
@@ -2592,7 +2592,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
                   xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
               `,x+1<f&&(A+=`
                   vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${c};
+                  xCOffset = xC + 1 + ${u};
                   if(xCOffset >= 0 && xCOffset < ${i}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
@@ -2604,7 +2604,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
                   xTexelR${b}C${x} = vec4(0.);
                 }
 
-                xCOffset = xC + ${c};
+                xCOffset = xC + ${u};
                 if(xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
                 } else {
@@ -2633,7 +2633,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }`,g="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${y}
 
-      const ivec2 strides = ivec2(${u}, ${c});
+      const ivec2 strides = ivec2(${c}, ${u});
       const ivec2 pads = ivec2(${o}, ${l});
 
       void main() {
@@ -2656,7 +2656,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${g}
         setOutput(result);
       }
-    `}};function GB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new $_(h):d=new F_(h),n.runWebGLProgram(d,[a,s],"float32")}var qB={kernelName:ks,backendName:"webgl",kernelFunc:GB},XB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function jB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new D_(h):d=new $_(h),n.runWebGLProgram(d,[a,s],"float32")}var GB={kernelName:ks,backendName:"webgl",kernelFunc:jB},qB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},KB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},XB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2736,15 +2736,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function ZB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new XB(h);return n.runWebGLProgram(d,[a,s],"float32")}var YB={kernelName:Hh,backendName:"webgl",kernelFunc:ZB};function JB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new KB(h);return n.runWebGLProgram(d,[a,s],"float32")}var QB={kernelName:Gh,backendName:"webgl",kernelFunc:JB},eV=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function KB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new qB(h);return n.runWebGLProgram(d,[a,s],"float32")}var ZB={kernelName:jh,backendName:"webgl",kernelFunc:KB};function YB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new XB(h);return n.runWebGLProgram(d,[a,s],"float32")}var JB={kernelName:Gh,backendName:"webgl",kernelFunc:YB},QB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function tV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=_e({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new eV(s),l=n.runWebGLProgram(o,[i],i.dtype),u=_e({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var nV={kernelName:qh,backendName:"webgl",kernelFunc:tV},rV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=`
+    `}};function eV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=_e({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new QB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=_e({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var tV={kernelName:qh,backendName:"webgl",kernelFunc:eV},nV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
-      const ivec2 pads = ivec2(${c}, ${h});
+      const ivec2 pads = ivec2(${u}, ${h});
       const float neg_infinity = -3.4e38;
 
       void main() {
@@ -2762,7 +2762,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
           if (hIn >= 0 && hIn < ${t}) {
             for (int w = 0; w < ${o}; w++) {
-              int wIn = wBeg + w * ${u};
+              int wIn = wBeg + w * ${c};
 
               if (wIn >= 0 && wIn < ${n}) {
                 float xVal = getX(batch, hIn, wIn, d1);
@@ -2780,7 +2780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function aV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new rV(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=_e({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var sV={kernelName:Sc,backendName:"webgl",kernelFunc:aV},iV="return (x >= 0.0) ? x : (exp(x) - 1.0);",oV=`
+    `}};function rV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new nV(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=_e({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var aV={kernelName:Sc,backendName:"webgl",kernelFunc:rV},sV="return (x >= 0.0) ? x : (exp(x) - 1.0);",iV=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2789,12 +2789,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,lV=Ye({opSnippet:iV,packedOpSnippet:oV}),cV={kernelName:xo,backendName:"webgl",kernelFunc:lV},uV="return (b >= 1.0) ? a : a * (b + 1.0);",hV=`
+`,oV=Ye({opSnippet:sV,packedOpSnippet:iV}),lV={kernelName:xo,backendName:"webgl",kernelFunc:oV},cV="return (b >= 1.0) ? a : a * (b + 1.0);",uV=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,dV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _u(hV,r.shape,a.shape):new Bl(uV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},pV={kernelName:Zh,backendName:"webgl",kernelFunc:dV},fV=`
+`,hV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _u(uV,r.shape,a.shape):new Bl(cV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},dV={kernelName:Zh,backendName:"webgl",kernelFunc:hV},pV=`
   return vec4(equal(a, b));
-`,mV="return float(a == b);",AV=an({opSnippet:mV,packedOpSnippet:fV,dtype:"bool"}),yV={kernelName:bo,backendName:"webgl",kernelFunc:AV},gV=`
+`,fV="return float(a == b);",mV=an({opSnippet:fV,packedOpSnippet:pV,dtype:"bool"}),AV={kernelName:bo,backendName:"webgl",kernelFunc:mV},yV=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -2809,7 +2809,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,xV=Ye({opSnippet:gV}),wV={kernelName:wo,backendName:"webgl",kernelFunc:xV},D_="return exp(x);",O_=Ye({opSnippet:D_,packedOpSnippet:D_,cpuKernelImpl:xP}),bV={kernelName:Ss,backendName:"webgl",kernelFunc:O_};function kA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),_e({inputs:{x:s},backend:r,attrs:{shape:o}})}var _V={kernelName:_o,backendName:"webgl",kernelFunc:kA},z_="return exp(x) - 1.0;",vV=Ye({opSnippet:z_,packedOpSnippet:z_,cpuKernelImpl:wP}),kV={kernelName:vo,backendName:"webgl",kernelFunc:vV},P_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,gV=Ye({opSnippet:yV}),xV={kernelName:wo,backendName:"webgl",kernelFunc:gV},O_="return exp(x);",z_=Ye({opSnippet:O_,packedOpSnippet:O_,cpuKernelImpl:gP}),wV={kernelName:Ss,backendName:"webgl",kernelFunc:z_};function SA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),_e({inputs:{x:s},backend:r,attrs:{shape:o}})}var bV={kernelName:_o,backendName:"webgl",kernelFunc:SA},P_="return exp(x) - 1.0;",_V=Ye({opSnippet:P_,packedOpSnippet:P_,cpuKernelImpl:xP}),vV={kernelName:vo,backendName:"webgl",kernelFunc:_V},L_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2842,13 +2842,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function L_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=_e({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new P_("real",l,t),c=new P_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=_e({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function IV(e){let{inputs:t,backend:n}=e,{input:r}=t;return L_(r,!1,n)}var SV={kernelName:Yh,backendName:"webgl",kernelFunc:IV},NV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function W_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=_e({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new L_("real",l,t),u=new L_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=_e({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function kV(e){let{inputs:t,backend:n}=e,{input:r}=t;return W_(r,!1,n)}var IV={kernelName:Yh,backendName:"webgl",kernelFunc:kV},SV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function IA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new NV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var TV={kernelName:Nc,backendName:"webgl",kernelFunc:IA},EV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function NA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new SV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var NV={kernelName:Nc,backendName:"webgl",kernelFunc:NA},TV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -2862,7 +2862,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},CV={kernelName:ko,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new EV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},W_="return floor(x);",RV=Ye({opSnippet:W_,packedOpSnippet:W_,cpuKernelImpl:bP}),MV={kernelName:Ns,backendName:"webgl",kernelFunc:RV},FV=`
+    `}},EV={kernelName:ko,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new TV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},B_="return floor(x);",CV=Ye({opSnippet:B_,packedOpSnippet:B_,cpuKernelImpl:wP}),RV={kernelName:Ns,backendName:"webgl",kernelFunc:CV},MV=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2872,7 +2872,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
   } else {
     return NAN;
   }
-`,$V=`
+`,FV=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2893,7 +2893,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,DV=an({opSnippet:FV,packedOpSnippet:$V,dtype:"int32"}),OV={kernelName:Ts,backendName:"webgl",kernelFunc:DV},zV=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,r]=e;this.outputShape=e,this.userCode=`
+`,$V=an({opSnippet:MV,packedOpSnippet:FV,dtype:"int32"}),DV={kernelName:Ts,backendName:"webgl",kernelFunc:$V},OV=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2915,7 +2915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},PV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,r]=e;this.outputShape=e,this.userCode=`
+    `}},zV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
 
         ${t.output} = result;
       }
-    `}},WV={kernelName:dd,backendName:"webgl",kernelFunc:LV},Ul;function LV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[u,l],h=[u,l,s];(o||i)&&(Ul==null&&(Ul=document.createElement("canvas").getContext("2d")),Ul.canvas.width=l,Ul.canvas.height=u,Ul.drawImage(a,0,0,l,u),a=Ul.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new PV(h):new zV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function BV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(c),A=R.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=T_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=E_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",S=p?kp(p,!1):null,T=new N_(A,b,S,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push(F),g.push(F)}y=n.runWebGLProgram(T,E,"float32")}let w=_e({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var VV={kernelName:oi,backendName:"webgl",kernelFunc:BV};function UV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?kp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let S;y?S=new $_(A,b,g,_,x):S=new F_(A,b,g,_,x);let T=n.runWebGLProgram(S,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var jV={kernelName:li,backendName:"webgl",kernelFunc:UV},HV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ut(t.length),a=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},LV={kernelName:dd,backendName:"webgl",kernelFunc:PV},Ul;function PV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(Ul==null&&(Ul=document.createElement("canvas").getContext("2d")),Ul.canvas.width=l,Ul.canvas.height=c,Ul.drawImage(a,0,0,l,c),a=Ul.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=Qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new zV(h):new OV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function WV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(u),A=R.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=E_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=C_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",S=p?kp(p,!1):null,T=new T_(A,b,S,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push(F),g.push(F)}y=n.runWebGLProgram(T,E,"float32")}let w=_e({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var BV={kernelName:oi,backendName:"webgl",kernelFunc:WV};function VV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?kp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let S;y?S=new D_(A,b,g,_,x):S=new $_(A,b,g,_,x);let T=n.runWebGLProgram(S,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var UV={kernelName:li,backendName:"webgl",kernelFunc:VV},HV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ut(t.length),a=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${r} strides = ${r}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -2960,21 +2960,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function GV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=R.prepareAndValidate(r,a),h=_e({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=_e({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new HV(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=_e({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var qV={kernelName:So,backendName:"webgl",kernelFunc:GV},KV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),r=XV(e,2);this.userCode=`
+      `}};function jV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=R.prepareAndValidate(r,a),h=_e({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=_e({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/c,c]}}),p=new HV(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=_e({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var GV={kernelName:So,backendName:"webgl",kernelFunc:jV},XV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),r=qV(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function XV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function ZV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=v.sizeFromShape(s.shape),h=[],d=_e({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=_e({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});h.push(d),h.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=_P(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new KV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=_e({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var YV={kernelName:Io,backendName:"webgl",kernelFunc:ZV},JV="return float(a > b);",QV=`
+    `}};function qV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function KV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=v.sizeFromShape(s.shape),h=[],d=_e({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=_e({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=bP(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new XV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=_e({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var ZV={kernelName:Io,backendName:"webgl",kernelFunc:KV},YV="return float(a > b);",JV=`
   return vec4(greaterThan(a, b));
-`,eU=an({opSnippet:JV,packedOpSnippet:QV,cpuKernelImpl:vP,dtype:"bool"}),tU={kernelName:No,backendName:"webgl",kernelFunc:eU},nU="return float(a >= b);",rU=`
+`,QV=an({opSnippet:YV,packedOpSnippet:JV,cpuKernelImpl:_P,dtype:"bool"}),eU={kernelName:No,backendName:"webgl",kernelFunc:QV},tU="return float(a >= b);",nU=`
   return vec4(greaterThanEqual(a, b));
-`,aU=an({opSnippet:nU,packedOpSnippet:rU,dtype:"bool"}),sU={kernelName:Cs,backendName:"webgl",kernelFunc:aU};function iU(e){let{inputs:t,backend:n}=e,{input:r}=t;return L_(r,!0,n)}var oU={kernelName:Jh,backendName:"webgl",kernelFunc:iU},lU="return float(!isnan(x) && !isinf(x));",cU=Ye({opSnippet:lU,dtype:"bool"}),uU={kernelName:To,backendName:"webgl",kernelFunc:cU},hU="return float(isinf(x));",dU=Ye({opSnippet:hU,dtype:"bool"}),pU={kernelName:Eo,backendName:"webgl",kernelFunc:dU},fU="return float(isnan(x));",mU=Ye({opSnippet:fU,dtype:"bool"}),AU={kernelName:Co,backendName:"webgl",kernelFunc:mU},yU="return float(a < b);",gU=`
+`,rU=an({opSnippet:tU,packedOpSnippet:nU,dtype:"bool"}),aU={kernelName:Cs,backendName:"webgl",kernelFunc:rU};function sU(e){let{inputs:t,backend:n}=e,{input:r}=t;return W_(r,!0,n)}var iU={kernelName:Jh,backendName:"webgl",kernelFunc:sU},oU="return float(!isnan(x) && !isinf(x));",lU=Ye({opSnippet:oU,dtype:"bool"}),cU={kernelName:To,backendName:"webgl",kernelFunc:lU},uU="return float(isinf(x));",hU=Ye({opSnippet:uU,dtype:"bool"}),dU={kernelName:Eo,backendName:"webgl",kernelFunc:hU},pU="return float(isnan(x));",fU=Ye({opSnippet:pU,dtype:"bool"}),mU={kernelName:Co,backendName:"webgl",kernelFunc:fU},AU="return float(a < b);",yU=`
   return vec4(lessThan(a, b));
-`,xU=an({opSnippet:yU,packedOpSnippet:gU,cpuKernelImpl:kP,dtype:"bool"}),wU={kernelName:Ro,backendName:"webgl",kernelFunc:xU},bU="return float(a <= b);",_U=`
+`,gU=an({opSnippet:AU,packedOpSnippet:yU,cpuKernelImpl:vP,dtype:"bool"}),xU={kernelName:Ro,backendName:"webgl",kernelFunc:gU},wU="return float(a <= b);",bU=`
   return vec4(lessThanEqual(a, b));
-`,vU=an({opSnippet:bU,packedOpSnippet:_U,dtype:"bool"}),kU={kernelName:Mo,backendName:"webgl",kernelFunc:vU};function IU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=IP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var SU={kernelName:ed,backendName:"webgl",kernelFunc:IU},NU=`if (x < 0.0) return NAN;
-  return log(x);`,TU=`
+`,_U=an({opSnippet:wU,packedOpSnippet:bU,dtype:"bool"}),vU={kernelName:Mo,backendName:"webgl",kernelFunc:_U};function kU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=kP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var IU={kernelName:ed,backendName:"webgl",kernelFunc:kU},SU=`if (x < 0.0) return NAN;
+  return log(x);`,NU=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2983,16 +2983,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,EU=Ye({opSnippet:NU,packedOpSnippet:TU,cpuKernelImpl:SP}),CU={kernelName:Fs,backendName:"webgl",kernelFunc:EU},RU="return log(1.0 + x);",MU=Ye({opSnippet:RU}),FU={kernelName:Fo,backendName:"webgl",kernelFunc:MU},$U="return float(a >= 1.0 && b >= 1.0);",DU=`
+`,TU=Ye({opSnippet:SU,packedOpSnippet:NU,cpuKernelImpl:IP}),EU={kernelName:Fs,backendName:"webgl",kernelFunc:TU},CU="return log(1.0 + x);",RU=Ye({opSnippet:CU}),MU={kernelName:Fo,backendName:"webgl",kernelFunc:RU},FU="return float(a >= 1.0 && b >= 1.0);",$U=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,OU=an({opSnippet:$U,packedOpSnippet:DU,dtype:"bool"}),zU={kernelName:$o,backendName:"webgl",kernelFunc:OU},PU="return float(!(x >= 1.0));",LU=Ye({opSnippet:PU}),WU={kernelName:Tc,backendName:"webgl",kernelFunc:LU},BU="return float(a >= 1.0 || b >= 1.0);",VU=`
+`,DU=an({opSnippet:FU,packedOpSnippet:$U,dtype:"bool"}),OU={kernelName:$o,backendName:"webgl",kernelFunc:DU},zU="return float(!(x >= 1.0));",PU=Ye({opSnippet:zU}),LU={kernelName:Tc,backendName:"webgl",kernelFunc:PU},WU="return float(a >= 1.0 || b >= 1.0);",BU=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,UU=an({opSnippet:BU,packedOpSnippet:VU,dtype:"bool"}),jU={kernelName:Ec,backendName:"webgl",kernelFunc:UU},HU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,VU=an({opSnippet:WU,packedOpSnippet:BU,dtype:"bool"}),UU={kernelName:Ec,backendName:"webgl",kernelFunc:VU},HU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3011,7 +3011,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},GU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},jU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3073,7 +3073,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},qU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new GU(a.shape,s,i,o,l):new HU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},XU={kernelName:Cc,backendName:"webgl",kernelFunc:qU},KU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
+    `}},GU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new jU(a.shape,s,i,o,l):new HU(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},qU={kernelName:Cc,backendName:"webgl",kernelFunc:GU},XU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3128,14 +3128,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
       }
       setOutput(result);
       }
-    `}},ZU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new KU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},YU={kernelName:td,backendName:"webgl",kernelFunc:ZU};function JU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=_e({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ci(i,e.dtype,"max",r),l=_e({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function B_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[c[x]];let b=gA(g,a.shape,a.dtype,c,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=Ip(a,c,n);u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("max",u,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,u),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=NP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=JU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var QU={kernelName:$s,backendName:"webgl",kernelFunc:B_},ej=a_+`
+    `}},KU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new XU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},ZU={kernelName:td,backendName:"webgl",kernelFunc:KU};function YU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=_e({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ci(i,e.dtype,"max",r),l=_e({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function V_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let b=wA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=Ip(a,u,n);c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("max",c,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=SP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=YU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var JU={kernelName:$s,backendName:"webgl",kernelFunc:V_},QU=s_+`
   return max(a, b);
-`,tj=`
+`,eH=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+vp+`
   return result;
-`,nj=an({opSnippet:ej,packedOpSnippet:tj,cpuKernelImpl:TP}),rj={kernelName:Ds,backendName:"webgl",kernelFunc:nj};function aj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Bn({inputs:{x:a},backend:n});let h=new vu(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var sj={kernelName:Os,backendName:"webgl",kernelFunc:aj};function ij(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new bA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var oj={kernelName:Rc,backendName:"webgl",kernelFunc:ij},lj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,tH=an({opSnippet:QU,packedOpSnippet:eH,cpuKernelImpl:NP}),nH={kernelName:Ds,backendName:"webgl",kernelFunc:tH};function rH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Bn({inputs:{x:a},backend:n});let h=new vu(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var aH={kernelName:Os,backendName:"webgl",kernelFunc:rH};function sH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new vA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var iH={kernelName:Rc,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3181,8 +3181,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},cj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=`
-      const ivec3 pads = ivec3(${c}, ${h}, ${d});
+    `}},lH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
+      const ivec3 pads = ivec3(${u}, ${h}, ${d});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -3218,7 +3218,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${u};
+            for (int wC = 0; wC < ${c};
                 wC += ${i}) {
               float dyC = float(dyCCorner + wC) / ${r}.0;
 
@@ -3235,8 +3235,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
               // Get the current value, check it against the value from the
               // position matrix.
               int curPosValue =
-                  wD * ${l} * ${u} +
-                  wR * ${u} + wC;
+                  wD * ${l} * ${c} +
+                  wR * ${c} + wC;
               float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
               dotProd += dyValue * mask;
@@ -3245,14 +3245,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new bA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new cj(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var hj={kernelName:rd,backendName:"webgl",kernelFunc:uj};function dj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Fl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new vu(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new lj(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var pj={kernelName:nd,backendName:"webgl",kernelFunc:dj};function fj(e,t,n,r){let a=new vu(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new vu(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var mj={kernelName:ad,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,a,s,u,i),[h,d]=fj(r,o,c,l);return[h,d]}};function Aj(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=_e({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ci(i,"float32","mean",r),l=_e({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var yj={kernelName:zs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=r.shape[c[S]];let _=gA(w,r.shape,r.dtype,c,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=Ip(r,c,i);p.push(f),u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("sum",u,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=Aj(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function gj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ci(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=_e({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=_e({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var xj={kernelName:Ps,backendName:"webgl",kernelFunc:gj},wj=a_+`
+    `}};function cH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new vA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new lH(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var uH={kernelName:rd,backendName:"webgl",kernelFunc:cH};function hH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Fl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new vu(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new oH(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var dH={kernelName:nd,backendName:"webgl",kernelFunc:hH};function pH(e,t,n,r){let a=new vu(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new vu(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var fH={kernelName:ad,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=R.computePool2DInfo(r.shape,a,s,c,i),[h,d]=pH(r,o,u,l);return[h,d]}};function mH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=_e({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ci(i,"float32","mean",r),l=_e({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var AH={kernelName:zs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=r.shape[u[S]];let _=wA(w,r.shape,r.dtype,u,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=Ip(r,u,i);p.push(f),c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=mH(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function yH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Ci(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=_e({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=_e({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var gH={kernelName:Ps,backendName:"webgl",kernelFunc:yH},xH=s_+`
   return min(a, b);
-`,bj=`
+`,wH=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+vp+`
   return result;
-`,_j=an({opSnippet:wj,packedOpSnippet:bj,cpuKernelImpl:EP}),vj={kernelName:Ls,backendName:"webgl",kernelFunc:_j},kj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=ut(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
+`,bH=an({opSnippet:xH,packedOpSnippet:wH,cpuKernelImpl:TP}),_H={kernelName:Ls,backendName:"webgl",kernelFunc:bH},vH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ut(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3281,7 +3281,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},Ij=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ut(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=An("rc",r),l=An("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
+    `}},kH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ut(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=An("rc",r),l=An("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3292,11 +3292,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
       `;d=`
         ${a} rc = outputLoc;
         ${p}
-        result[0] = getChannel(getX(${l.join()}), ${c});
+        result[0] = getChannel(getX(${l.join()}), ${u});
         ${o[r-1]} += 1;
-        if(${u}) {
+        if(${c}) {
           ${p}
-          result[1] = getChannel(getX(${l.join()}), ${c});
+          result[1] = getChannel(getX(${l.join()}), ${u});
         }
       `}else{let p=`
         ${a} source = rc;
@@ -3310,21 +3310,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
       `;d=`
         ${a} rc = outputLoc;
         ${p}
-        result[0] = getChannel(getX(${l.join()}), ${c});
+        result[0] = getChannel(getX(${l.join()}), ${u});
         ${o[r-1]} += 1;
-        if(${u}) {
+        if(${c}) {
           ${p}
-          result[1] = getChannel(getX(${l.join()}), ${c});
+          result[1] = getChannel(getX(${l.join()}), ${u});
         }
         rc = outputLoc;
         ${o[r-2]} += 1;
         if(${o[r-2]} < ${this.outputShape[r-2]}) {
           ${p}
-          result[2] = getChannel(getX(${l.join()}), ${c});
+          result[2] = getChannel(getX(${l.join()}), ${u});
           ${o[r-1]} += 1;
-          if(${u}) {
+          if(${c}) {
             ${p}
-            result[3] = getChannel(getX(${l.join()}), ${c});
+            result[3] = getChannel(getX(${l.join()}), ${u});
           }
         }
       `}this.userCode=`
@@ -3337,13 +3337,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},Sj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ij(r.shape,a,s):new kj(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},Nj={kernelName:Mc,backendName:"webgl",kernelFunc:Sj},Tj=`if (b == 0.0) return NAN;
-  return mod(a, b);`,Ej=`
+    `}},IH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kH(r.shape,a,s):new vH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},SH={kernelName:Mc,backendName:"webgl",kernelFunc:IH},NH=`if (b == 0.0) return NAN;
+  return mod(a, b);`,TH=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+vp+`
   return result;
-`,Cj=an({opSnippet:Tj,packedOpSnippet:Ej}),Rj={kernelName:Do,backendName:"webgl",kernelFunc:Cj},Mj=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,EH=an({opSnippet:NH,packedOpSnippet:TH}),CH={kernelName:Do,backendName:"webgl",kernelFunc:EH},RH=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3365,11 +3365,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wW=Ye({opSnippet:xW}),bW={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},Fj=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},MH=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,$j=`
+return a / b;`,FH=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3387,14 +3387,14 @@ return a / b;`,$j=`
   }
 
   return result;
-`,V_=an({opSnippet:Fj,packedOpSnippet:$j,checkOutOfBounds:!0}),Dj={kernelName:Is,backendName:"webgl",kernelFunc:V_},U_="return a - b;",j_=an({opSnippet:U_,packedOpSnippet:U_,supportsComplex:!0,cpuKernelImpl:zP}),Oj={kernelName:ri,backendName:"webgl",kernelFunc:j_};function H_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=B_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=_e({inputs:{x:o},backend:n,attrs:{shape:l}}),c=j_({inputs:{a,b:u},backend:n}),h=O_({inputs:{x:c},backend:n}),d=wA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=_e({inputs:{x:d},backend:n,attrs:{shape:l}}),f=V_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var zj={kernelName:ti,backendName:"webgl",kernelFunc:H_};function Pj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:H_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new Mj(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var Lj={kernelName:sd,backendName:"webgl",kernelFunc:Pj},G_="return -x;";function Wj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=RP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,G_):a=new Ga(r.shape,G_),n.runWebGLProgram(a,[r],r.dtype)}var Bj={kernelName:Oo,backendName:"webgl",kernelFunc:Wj},Vj=jr.nonMaxSuppressionV3Impl;function Uj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=Vj(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var jj={kernelName:Po,backendName:"webgl",kernelFunc:Uj},Hj=jr.nonMaxSuppressionV4Impl;function Gj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=Hj(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var qj={kernelName:Lo,backendName:"webgl",kernelFunc:Gj},Xj=jr.nonMaxSuppressionV5Impl;function Kj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=Xj(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Zj={kernelName:Wo,backendName:"webgl",kernelFunc:Kj},Yj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,U_=an({opSnippet:MH,packedOpSnippet:FH,checkOutOfBounds:!0}),$H={kernelName:Is,backendName:"webgl",kernelFunc:U_},H_="return a - b;",j_=an({opSnippet:H_,packedOpSnippet:H_,supportsComplex:!0,cpuKernelImpl:OP}),DH={kernelName:ri,backendName:"webgl",kernelFunc:j_};function G_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=V_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),c=_e({inputs:{x:o},backend:n,attrs:{shape:l}}),u=j_({inputs:{a,b:c},backend:n}),h=z_({inputs:{x:u},backend:n}),d=_A({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=_e({inputs:{x:d},backend:n,attrs:{shape:l}}),f=U_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var OH={kernelName:ti,backendName:"webgl",kernelFunc:G_};function zH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:G_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new RH(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var PH={kernelName:sd,backendName:"webgl",kernelFunc:zH},q_="return -x;";function LH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=CP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,q_):a=new Ga(r.shape,q_),n.runWebGLProgram(a,[r],r.dtype)}var WH={kernelName:Oo,backendName:"webgl",kernelFunc:LH},BH=Hr.nonMaxSuppressionV3Impl;function VH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=BH(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var UH={kernelName:Po,backendName:"webgl",kernelFunc:VH},HH=Hr.nonMaxSuppressionV4Impl;function jH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=HH(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var GH={kernelName:Lo,backendName:"webgl",kernelFunc:jH},qH=Hr.nonMaxSuppressionV5Impl;function XH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=qH(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var KH={kernelName:Wo,backendName:"webgl",kernelFunc:XH},ZH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${r}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},Jj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new Yj(l,s,i,o),c=_e({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=_e({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},Qj={kernelName:Bs,backendName:"webgl",kernelFunc:Jj};function Cp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Iu({inputs:{input:r},backend:n}),s=Cp({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return IA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var eH={kernelName:al,backendName:"webgl",kernelFunc:Cp};function q_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Iu({inputs:{input:r},backend:n}),s=q_({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return IA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var tH={kernelName:Bo,backendName:"webgl",kernelFunc:q_};function nH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return kA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=kA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=S_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var rH={kernelName:Vo,backendName:"webgl",kernelFunc:nH},aH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=ut(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
+    `}},YH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),c=new ZH(l,s,i,o),u=_e({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=_e({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},JH={kernelName:Bs,backendName:"webgl",kernelFunc:YH};function Cp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Iu({inputs:{input:r},backend:n}),s=Cp({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return NA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var QH={kernelName:al,backendName:"webgl",kernelFunc:Cp};function X_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Iu({inputs:{input:r},backend:n}),s=X_({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return NA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var ej={kernelName:Bo,backendName:"webgl",kernelFunc:X_};function tj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return SA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=SA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=N_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var nj={kernelName:Vo,backendName:"webgl",kernelFunc:tj},rj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ut(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
         uniform float value;
@@ -3421,19 +3421,19 @@ return a / b;`,$j=`
           setOutput(getX(${o}));
         }
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},sH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ut(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=An("rc",r),l=An("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
-       if(${u}) {
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},aj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ut(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=An("rc",r),l=An("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
+       if(${c}) {
       `,r===1?"":`}
        rc = outputLoc;
        ${o[r-2]} += 1;
        if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":`  ${o[r-1]} += 1;
-         if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
+         if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
         ${h[f]}
         if (${d}) {
           result[${f}] = float(value);
         } else {
           ${a} source = rc - start;
-          result[${f}] = getChannel(getX(${l.join()}), ${c});
+          result[${f}] = getChannel(getX(${l.join()}), ${u});
         }
       `;p+=r===1?"} ":"}}",this.userCode=`
       const ${a} start = ${a}(${s});
@@ -3446,7 +3446,7 @@ return a / b;`,$j=`
         ${p}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},X_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sH(a.shape,s,i):new aH(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},iH={kernelName:Vs,backendName:"webgl",kernelFunc:X_},oH=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},K_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aj(a.shape,s,i):new rj(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},sj={kernelName:Vs,backendName:"webgl",kernelFunc:K_},ij=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3455,7 +3455,7 @@ return a / b;`,$j=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,lH=`
+`,oj=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3471,9 +3471,9 @@ return a / b;`,$j=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+vp+`
   return result;
-`,cH=an({opSnippet:oH,packedOpSnippet:lH}),uH={kernelName:Us,backendName:"webgl",kernelFunc:cH};function hH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=R.getAxesPermutation(c,o),d=a;h!=null&&(d=En({inputs:{x:a},backend:n,attrs:{perm:h}}),c=R.getInnerMostAxes(c.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=MP(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=_e({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=yd(a.dtype),w=Ci(y,g,"prod",n);p=_e({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=_e({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var dH={kernelName:Uo,backendName:"webgl",kernelFunc:hH},K_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=FP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},pH={kernelName:Fc,backendName:"webgl",kernelFunc:K_},fH="return 1.0 / x;",mH=Ye({opSnippet:fH}),AH={kernelName:jo,backendName:"webgl",kernelFunc:mH},yH=kr+`
+`,lj=an({opSnippet:ij,packedOpSnippet:oj}),cj={kernelName:Us,backendName:"webgl",kernelFunc:lj};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=v.parseAxisParam(s,a.shape),u=c,h=R.getAxesPermutation(u,o),d=a;h!=null&&(d=En({inputs:{x:a},backend:n,attrs:{perm:h}}),u=R.getInnerMostAxes(u.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=RP(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,u),A=v.sizeFromShape(m),y=_e({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=yd(a.dtype),w=Ci(y,g,"prod",n);p=_e({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,c);p=_e({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var hj={kernelName:Uo,backendName:"webgl",kernelFunc:uj},Z_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=MP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},dj={kernelName:Fc,backendName:"webgl",kernelFunc:Z_},pj="return 1.0 / x;",fj=Ye({opSnippet:pj}),mj={kernelName:Ho,backendName:"webgl",kernelFunc:fj},Aj=kr+`
   return (x < 0.0) ? 0.0 : x;
-`,gH=`
+`,yj=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3483,9 +3483,9 @@ return a / b;`,$j=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,xH=Ye({opSnippet:yH,packedOpSnippet:gH}),wH={kernelName:Hs,backendName:"webgl",kernelFunc:xH},bH=kr+`
+`,gj=Ye({opSnippet:Aj,packedOpSnippet:yj}),xj={kernelName:js,backendName:"webgl",kernelFunc:gj},wj=kr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,_H=`
+`,bj=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3495,10 +3495,10 @@ return a / b;`,$j=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,vH=Ye({opSnippet:bH,packedOpSnippet:_H}),kH={kernelName:qs,backendName:"webgl",kernelFunc:vH},IH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,_j=Ye({opSnippet:wj,packedOpSnippet:bj}),vj={kernelName:qs,backendName:"webgl",kernelFunc:_j},kj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${u[0]/c[0]},
-          ${u[1]/c[1]});
+          ${c[0]/u[0]},
+          ${c[1]/u[1]});
       const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
 
       void main() {
@@ -3528,11 +3528,11 @@ return a / b;`,$j=`
 
         setOutput(newValue);
       }
-    `}},SH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},Ij=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${u[0]/c[0]},
-          ${u[1]/c[1]},
-          ${u[1]/c[1]});
+          ${c[0]/u[0]},
+          ${c[1]/u[1]},
+          ${c[1]/u[1]});
       const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                      ${o}.0);
 
@@ -3605,7 +3605,7 @@ return a / b;`,$j=`
 
         setOutput(newValue);
       }
-    `}};function NH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new SH(a.shape,l,u,s,i):new IH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var TH={kernelName:Gs,backendName:"webgl",kernelFunc:NH},EH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function Sj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ij(a.shape,l,c,s,i):new kj(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var Nj={kernelName:Gs,backendName:"webgl",kernelFunc:Sj},Tj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3615,8 +3615,8 @@ return a / b;`,$j=`
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${u});
-        const float widthScale = float(${c});
+        const float heightScale = float(${c});
+        const float widthScale = float(${u});
 
         const float invHeightScale = float(${h});
         const float invWidthScale = float(${d});
@@ -3686,10 +3686,10 @@ return a / b;`,$j=`
 
         setOutput(accumulator);
       }
-    `}};function CH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new EH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var RH={kernelName:ld,backendName:"webgl",kernelFunc:CH},MH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function Ej(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Tj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Cj={kernelName:ld,backendName:"webgl",kernelFunc:Ej},Rj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${u[0]/c[0]},
-          ${u[1]/c[1]});
+          ${c[0]/u[0]},
+          ${c[1]/u[1]});
       const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
 
       void main() {
@@ -3708,7 +3708,7 @@ return a / b;`,$j=`
 
         setOutput(newValue);
       }
-    `}};function FH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new MH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var $H={kernelName:$c,backendName:"webgl",kernelFunc:FH},DH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new Rj(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var Fj={kernelName:$c,backendName:"webgl",kernelFunc:Mj},$j=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3718,8 +3718,8 @@ return a / b;`,$j=`
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${u});
-        const float widthScale = float(${c});
+        const float heightScale = float(${c});
+        const float widthScale = float(${u});
 
         const float invHeightScale = float(${h});
         const float invWidthScale = float(${d});
@@ -3778,7 +3778,7 @@ return a / b;`,$j=`
 
         setOutput(accumulator);
       }
-    `}};function OH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new DH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var zH={kernelName:od,backendName:"webgl",kernelFunc:OH},PH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function Dj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new $j(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Oj={kernelName:od,backendName:"webgl",kernelFunc:Dj},zj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -3788,7 +3788,7 @@ return a / b;`,$j=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},LH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=An("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
+    `}},Pj=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=An("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3809,14 +3809,14 @@ return a / b;`,$j=`
             result.g = ${l(r.slice())};
           }
           if(${s}) {
-            result.b = ${u(r.slice())};
+            result.b = ${c(r.slice())};
             if(${a}) {
-              result.a = ${c(r.slice())};
+              result.a = ${u(r.slice())};
             }
           }
           setOutput(result);
         }
-    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function WH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Bn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LH(a.shape,o):new PH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var BH={kernelName:Xs,backendName:"webgl",kernelFunc:WH},VH=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Lj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Bn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pj(a.shape,o):new zj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Wj={kernelName:Xs,backendName:"webgl",kernelFunc:Lj},Bj=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         uniform vec4 params;
@@ -3836,7 +3836,7 @@ return a / b;`,$j=`
           }
           setOutput(outputValue);
         }
-    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},UH={kernelName:sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new VH(r.shape,s),[u,c]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},jH=`
+    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Vj={kernelName:sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Bj(r.shape,s),[c,u]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Uj=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3851,7 +3851,7 @@ return a / b;`,$j=`
       return base + 1.0;
     }
   }
-`,HH=Ye({opSnippet:jH}),GH={kernelName:Ks,backendName:"webgl",kernelFunc:HH},qH="return inversesqrt(x);",XH=Ye({opSnippet:qH,cpuKernelImpl:$P}),KH={kernelName:Zs,backendName:"webgl",kernelFunc:XH},Z_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(a.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
+`,Hj=Ye({opSnippet:Uj}),jj={kernelName:Ks,backendName:"webgl",kernelFunc:Hj},Gj="return inversesqrt(x);",qj=Ye({opSnippet:Gj,cpuKernelImpl:FP}),Xj={kernelName:Zs,backendName:"webgl",kernelFunc:qj},Y_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(a.length),l=ut(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -3861,7 +3861,7 @@ return a / b;`,$j=`
           for (int i = 0; i < ${e}; i++) {
             int flattenedIndex = 0;
             for (int j = 0; j < ${t}; j++) {
-              int index = round(${c});
+              int index = round(${u});
               flattenedIndex += index * ${p};
             }
             if (flattenedIndex == coords[0]) {
@@ -3871,7 +3871,7 @@ return a / b;`,$j=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function ZH(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=_e({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=_e({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new Z_(l,o,p.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=_e({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var YH={kernelName:Go,backendName:"webgl",kernelFunc:ZH},JH=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);r=o.join(),a=l.join()}let s=ut(n);this.userCode=`
+      `}};function Kj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=R.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=_e({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=_e({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new Y_(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=_e({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var Zj={kernelName:Go,backendName:"webgl",kernelFunc:Kj},Yj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ut(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${r});
@@ -3881,21 +3881,21 @@ return a / b;`,$j=`
           setOutput(getB(${a}));
         }
       }
-    `}};function QH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new JH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],or(a.dtype,s.dtype))}var eG={kernelName:qo,backendName:"webgl",kernelFunc:QH},tG=`
+    `}};function Jj(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Yj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],or(a.dtype,s.dtype))}var Qj={kernelName:qo,backendName:"webgl",kernelFunc:Jj},eG=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${R.SELU_SCALEALPHA};
   float scale = ${R.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,nG=Ye({opSnippet:tG}),rG={kernelName:Xo,backendName:"webgl",kernelFunc:nG},aG="return 1.0 / (1.0 + exp(-1.0 * x));",sG=Ye({opSnippet:aG}),iG={kernelName:Js,backendName:"webgl",kernelFunc:sG},oG=`
+`,tG=Ye({opSnippet:eG}),nG={kernelName:Xo,backendName:"webgl",kernelFunc:tG},rG="return 1.0 / (1.0 + exp(-1.0 * x));",aG=Ye({opSnippet:rG}),sG={kernelName:Js,backendName:"webgl",kernelFunc:aG},iG=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,lG=Ye({opSnippet:oG}),cG={kernelName:Yo,backendName:"webgl",kernelFunc:lG},uG=c_+`
+`,oG=Ye({opSnippet:iG}),lG={kernelName:Yo,backendName:"webgl",kernelFunc:oG},cG=u_+`
   return sin(x);
-`,hG=Ye({opSnippet:uG}),dG={kernelName:Ys,backendName:"webgl",kernelFunc:hG},pG=`
+`,uG=Ye({opSnippet:cG}),hG={kernelName:Ys,backendName:"webgl",kernelFunc:uG},dG=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,fG=Ye({opSnippet:pG}),mG={kernelName:Zo,backendName:"webgl",kernelFunc:fG},AG=`
+`,pG=Ye({opSnippet:dG}),fG={kernelName:Zo,backendName:"webgl",kernelFunc:pG},mG=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3915,9 +3915,9 @@ return a / b;`,$j=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,yG=Ye({opSnippet:AG}),gG={kernelName:Jo,backendName:"webgl",kernelFunc:yG},xG=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],c=X_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(c.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(c.shape,s,o,!1),f=_e({inputs:{x:c},backend:n,attrs:{shape:h}}),m=En({inputs:{x:f},backend:n,attrs:{perm:d}}),A=_e({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},wG={kernelName:Dc,backendName:"webgl",kernelFunc:xG};function bG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new Z_(u,l,a.shape.length,s.shape.length,c,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=_e({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var _G={kernelName:cd,backendName:"webgl",kernelFunc:bG};function vG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=ku({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,f})}var kG={kernelName:Qo,backendName:"webgl",kernelFunc:vG},IG="return sqrt(x);",SG=Ye({opSnippet:IG}),NG={kernelName:Qs,backendName:"webgl",kernelFunc:SG},TG="return x * x;",EG=Ye({opSnippet:TG}),CG={kernelName:Oc,backendName:"webgl",kernelFunc:EG},Y_="return (a - b) * (a - b);",RG=an({opSnippet:Y_,packedOpSnippet:Y_}),MG={kernelName:ni,backendName:"webgl",kernelFunc:RG};function FG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=kr+`
+`,AG=Ye({opSnippet:mG}),yG={kernelName:Jo,backendName:"webgl",kernelFunc:AG},gG=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=K_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(u.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(u.shape,s,o,!1),f=_e({inputs:{x:u},backend:n,attrs:{shape:h}}),m=En({inputs:{x:f},backend:n,attrs:{perm:d}}),A=_e({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},xG={kernelName:Dc,backendName:"webgl",kernelFunc:gG};function wG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new Y_(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=_e({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var bG={kernelName:cd,backendName:"webgl",kernelFunc:wG};function _G(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=ku({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var vG={kernelName:Qo,backendName:"webgl",kernelFunc:_G},kG="return sqrt(x);",IG=Ye({opSnippet:kG}),SG={kernelName:Qs,backendName:"webgl",kernelFunc:IG},NG="return x * x;",TG=Ye({opSnippet:NG}),EG={kernelName:Oc,backendName:"webgl",kernelFunc:TG},J_="return (a - b) * (a - b);",CG=an({opSnippet:J_,packedOpSnippet:J_}),RG={kernelName:ni,backendName:"webgl",kernelFunc:CG};function MG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=kr+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new Ga(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var $G={kernelName:Fa,backendName:"webgl",kernelFunc:FG},DG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ut(n.length),s=ut(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
+  `,s=new Ga(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var FG={kernelName:Fa,backendName:"webgl",kernelFunc:MG},$G=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ut(n.length),s=ut(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -3925,15 +3925,15 @@ return a / b;`,$j=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function OG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=dn.sliceInfo(a.shape,s,i,o,l,u,c,h,d),w=_e({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=ku({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=_e({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,S=Ue(w.shape,w.dtype,x),T=OP(g,S,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new DG(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=_e({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var zG={kernelName:el,backendName:"webgl",kernelFunc:OG},PG="return tan(x);",LG=Ye({opSnippet:PG}),WG={kernelName:tl,backendName:"webgl",kernelFunc:LG},BG=`
+    `}};function DG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=dn.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=_e({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=ku({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=_e({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,S=Ue(w.shape,w.dtype,x),T=DP(g,S,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new $G(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=_e({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var OG={kernelName:el,backendName:"webgl",kernelFunc:DG},zG="return tan(x);",PG=Ye({opSnippet:zG}),LG={kernelName:tl,backendName:"webgl",kernelFunc:PG},WG=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,VG=Ye({opSnippet:BG}),UG={kernelName:ai,backendName:"webgl",kernelFunc:VG},HG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=jG(e);this.userCode=`
+`,BG=Ye({opSnippet:WG}),VG={kernelName:ai,backendName:"webgl",kernelFunc:BG},HG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=UG(e);this.userCode=`
       void main() {
         ${r} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function jG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function J_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(c=>v.decodeString(c)),l=Ue(a.shape,a.dtype,o),u=PP(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new HG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var GG={kernelName:Ma,backendName:"webgl",kernelFunc:J_};function qG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=LP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var XG={kernelName:nl,backendName:"webgl",kernelFunc:qG},KG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+    `}};function UG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function Q_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>v.decodeString(u)),l=Ue(a.shape,a.dtype,o),c=zP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new HG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var jG={kernelName:Ma,backendName:"webgl",kernelFunc:Q_};function GG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=PP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var qG={kernelName:nl,backendName:"webgl",kernelFunc:GG},XG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4045,7 +4045,7 @@ return a / b;`,$j=`
               }
               setOutput(outputValue);
             }
-        `}};function ZG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=new KG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var YG={kernelName:ud,backendName:"webgl",kernelFunc:ZG};function JG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Fl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=WP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var QG={kernelName:hd,backendName:"webgl",kernelFunc:JG};function eq(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==s&&(u[c++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=ku({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=_e({inputs:{x:A},backend:n,attrs:{shape:u}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var tq={kernelName:rl,backendName:"webgl",kernelFunc:eq},nq=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,h=`
+        `}};function KG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=r,[u,h,d,p]=a.shape,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=new XG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var ZG={kernelName:ud,backendName:"webgl",kernelFunc:KG};function YG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Fl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=LP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var JG={kernelName:hd,backendName:"webgl",kernelFunc:YG};function QG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=ku({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=_e({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var eq={kernelName:rl,backendName:"webgl",kernelFunc:QG},tq=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
         sumValue += dot(values, segFilter);
     `,d="";a%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4078,7 +4078,7 @@ return a / b;`,$j=`
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${u}; i += 4) {
+        for (int i = 0; i < ${c}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -4097,8 +4097,8 @@ return a / b;`,$j=`
           ${h}
         }
 
-        int inIdx = inOffset + ${u};
-        if (${c===1}) {
+        int inIdx = inOffset + ${c};
+        if (${u===1}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             initializationValue,
@@ -4116,7 +4116,7 @@ return a / b;`,$j=`
           );
 
           ${h}
-        } else if (${c===2}) {
+        } else if (${u===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -4132,7 +4132,7 @@ return a / b;`,$j=`
           );
 
           ${h}
-        } else if (${c===3}) {
+        } else if (${u===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -4151,26 +4151,26 @@ return a / b;`,$j=`
         }
         setOutput(${l});
       }
-    `}};function rq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],u=0,c=R.getAxesPermutation([u],o),h=a;c!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:c}}),l.push(h),u=R.getInnerMostAxes(1,o)[0]);let d=R.segment_util.computeOutShape(h.shape,u,i),p=v.sizeFromShape([h.shape[u]]),f=_e({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=yd(a.dtype),A=(b,_,x,S,T)=>{let E=b.shape[0],F=b.shape[1],P=R.segment_util.segOpComputeOptimalWindowSize(F,T),W={windowSize:P,inSize:F,batchSize:E,numSegments:T},V=new nq(W,_),U=n.compileAndRun(V,[b,x],S);if(l.push(U),U.shape[1]===T)return U;let H=K_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=J_({inputs:{x:H},backend:n,attrs:{reps:[F/P]}});return l.push(H),l.push(X),A(U,_,X,S,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=_e({inputs:{x:y},backend:n,attrs:{shape:d}}),w=g;if(c!=null){l.push(g);let b=R.getUndoAxesPermutation(c);w=En({inputs:{x:w},backend:n,attrs:{perm:b}})}return 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;qr(w===0,"input layer has >1 nodes"),qr(b===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof ql))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,w,b,_,x)=>{(b==null||_==null||x==null)&&(b=y.sourceLayer,_=y.nodeIndex,x=y.tensorIndex);let S=b.inboundNodes[_];if(w.indexOf(S)!==-1)throw new Nr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(g.indexOf(S)!==-1)return;this.containerNodes.add(Yr.nodeKey(b,_)),b.id in s||(s[b.id]=Object.keys(s).length),w.indexOf(S)===-1&&w.push(S);let T=S.inboundLayers.length;for(let E=0;E<T;E++){let F=S.inputTensors[E],P=S.inboundLayers[E],W=S.nodeIndices[E],V=S.tensorIndices[E];o(F,g,w,P,W,V)}for(g.push(S);w.indexOf(S)>=0;)w.splice(w.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let c=i.slice().reverse();for(let y of c){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],w=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,w),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let b=0;b<y.inboundLayers.length;b++){let _=y.inboundLayers[b],x=y.nodeIndices[b],S=_.inboundNodes[x],T=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(g+1,T),n[S.id]=S}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let d={};for(let y in r){let g=r[y];g in d||(d[g]=[]),d[g].push(a[y])}let p=Object.keys(d).map(y=>parseInt(y,10)).sort(Op);this.layers=[];for(let y of p){let g=d[y];g.sort((w,b)=>{let _=s[w.id],x=s[b.id];return _<x?-1:_>x?1:0});for(let w of g)w instanceof Yr&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(Op);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let w=g.outboundLayer;if(w!=null){for(let b of g.inputTensors)if(f.indexOf(b)===-1)throw new Nr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${w.name}". 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function X7(e,t){return Dte(e,t,"classWeight")}async function K7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());Ie(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let p=0;p<this.inputs.length;++p)u.push({key:this.inputs[p],value:n[p]});let c=new zi(u),h=Wu(this.outputs,c,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Ote(f,a[p]));let m=Nt(f);t.push(m),p===0?d=f:d=ie(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=Nt(m(r[A],h[A]))}qt(f),s.push(f)}return d=Nt(d),this.calculateLosses().forEach(p=>{d=ie(d,p)}),d},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new zi(s),o=Wu(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=Nt(u(a[l],o[l]));l===0?n=c:n=ie(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],h=Nt(u(a[c],o[c]));t.push(h)}return t})}async fit(e,t,n={}){return jte(this,e,t,n)}async fitDataset(e,t){return Wte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Ie(s),Rn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=_d().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-_d().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ma(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ma(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ma(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ma(r0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ma(r0(e)));{let e={};for(let t in this.metrics)e[t]=ma(r0(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Lu(e.optimizer_config),n=Cr(t),r;if(typeof e.loss=="string")r=Mi(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>Mi(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=Mi(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>Mi(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=Mi(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=kn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await kn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Kte,generatedBy:`TensorFlow.js tfjs-layers v${uy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await kn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=kn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;j7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){j7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Aa.className="Model";ae.registerClass(Aa);var rv=class extends Aa{};rv.className="Functional";ae.registerClass(rv);async function Zte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Lu(n),a=Cr(r,t);if(e.weightsManifest!=null){let s=await kn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),Ie(s)}return a}async function Jte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=kn.getLoadHandlers(e,t);if(n.length===0)n.push(kn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Yte(e,void 0,t)}async function Yte(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=Cr(Lu(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Zl))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Cr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};vy.className="ThresholdedReLU";ae.registerClass(vy);var ky=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ay().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Be(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ky.className="Softmax";ae.registerClass(ky);function Yl(e,t,n){if(typeof e=="number")return Ri(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(s==="channelsFirst"&&(e=st(e,[0,2,1])),a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Sd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Kr(o,n)),o})}function wv(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Sr()),Rt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Iy(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ja.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=st(l,[0,3,1,2])),l})}function one(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Sr()),Rt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=xv(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=fm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Kr(o,n)),s==="channelsFirst"&&(o=st(o,[0,4,1,2,3])),o})}var Sy=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Sy.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yl(t.kernelSize,e,"kernelSize"),this.strides=Yl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,er(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Qa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=jt(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=Yl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(qr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!$A(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ja(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Uu=class extends Sy{constructor(e,t){super(e,t);this.kernel=null,Uu.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=jt(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Be(e);let n,r=this.bias==null?null:this.bias.read(),a=s7(this.activation.getClassName());if(a!=null&&this.rank===2)n=wv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=ine(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=wv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=one(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ht(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Rr(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Tt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Ut(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},ju=class extends Uu{constructor(e){super(2,e);ju.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!$A(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};ju.className="Conv2D";ae.registerClass(ju);var i0=class extends Uu{constructor(e){super(3,e);i0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};i0.className="Conv3D";ae.registerClass(i0);var Ny=class extends ju{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Be(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=s0(o,h,u,this.padding),f=s0(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=st(n,[0,2,3,1]));let A=Nd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=st(A,[0,3,1,2])),this.bias!=null&&(A=Kr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ht(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=s0(t[r],o,s,this.padding),t[a]=s0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ny.className="Conv2DTranspose";ae.registerClass(Ny);var bv=class extends Uu{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=jt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=jt(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Be(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=st(e,[0,2,3,1])),n=Mm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Kr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=st(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};bv.className="SeparableConv";var Ty=class extends bv{constructor(e){super(2,e)}};Ty.className="SeparableConv2D";ae.registerClass(Ty);var o0=class extends Uu{constructor(e){super(1,e);o0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!$A(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};o0.className="Conv1D";ae.registerClass(o0);var Ey=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Be(e),this.dataFormat==="channelsLast"){let n=zp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=zp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Cropping2D";ae.registerClass(Ey);var Cy=class extends Ze{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,TQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Be(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=st(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return st(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="UpSampling2D";ae.registerClass(Cy);function lne(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Sr()),Rt(a);let i=Iy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=xl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=st(i,[0,3,1,2])),i})}var Ry=class extends Sy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=jt(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Be(e);let n=lne(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Kr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Rr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Rr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};Ry.className="DepthwiseConv2D";ae.registerClass(Ry);function _v(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function vv(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Tr(2,l));if(t=st(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=tn(a,-1)),a=st(a,u)),r&&(t=Ln(t,0),a!=null&&(a=Ln(a,0)));let c=[],h,d=n,p=t.shape[0],f=ur(t),m;a!=null&&(m=ur(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=Pn(_).sub(_),S=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,F)=>w[1][F].mul(_).add(E.mul(x)));return{output:S,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=pn(c,1)),[h,A,d]})}var Zr=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new l0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Tr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ey(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");ey(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new fa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ft([n,r])):this.states_=[Ft([n,this.cell.stateSize])];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ft([n,r])):this.states_[0]=Ft([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ie(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>qt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=_v(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Er){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Be(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=vv((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return z(()=>{let t=Ft(e.shape);return t=Me(t,[1,2]),t=$u(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?WA(t,[1,n]):t):this.cell.stateSize>1?[WA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Cr(r,n);return new e(Object.assign(t,{cell:a}))}};Zr.className="RNN";ae.registerClass(Zr);var zu=class extends Ze{},c0=class extends zu{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Gl([1,Za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Xr(O(e,s),this.kernel.read()):a=Xr(e,this.kernel.read()),this.bias!=null&&(a=Kr(a,this.bias.read())),i!=null&&(n=O(n,i));let o=ie(a,Xr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ja(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};c0.className="SimpleRNNCell";ae.registerClass(c0);var My=class extends Zr{constructor(e){e.cell=new c0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};My.className="SimpleRNN";ae.registerClass(My);var u0=class extends zu{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Gl([1,Za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let u=Xr(e,this.kernel.read());this.useBias&&(u=Kr(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let c=this.recurrentKernel.read(),[h,d]=Bt(c,[2*this.units,this.units],c.rank-1),p=Xr(r,h),[f,m,A]=Bt(u,3,u.rank-1),[y,g]=Bt(p,2,p.rank-1);i=this.recurrentActivation.apply(ie(f,y)),o=this.recurrentActivation.apply(ie(m,g));let w=Xr(O(o,r),d);l=this.activation.apply(ie(A,w));let b=ie(O(i,r),O(ie(1,St(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ja(this.activation),recurrentActivation:Ja(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};u0.className="GRUCell";ae.registerClass(u0);var Fy=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new u0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="GRU";ae.registerClass(Fy);var Hu=class extends zu{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Gl([1,Za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ht(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends pr{apply(i,o){let l=a.apply([s]),u=new Lp().apply([s]),c=a.apply([s*2]);return m7(m7(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Xr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=ie(h,Xr(r,this.recurrentKernel.read())),this.useBias&&(h=Kr(h,this.bias.read()));let[d,p,f,m]=Bt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),u=ie(O(l,a),O(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let A=O(c,this.activation.apply(u));return[A,A,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ja(this.activation),recurrentActivation:Ja(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Hu.className="LSTMCell";ae.registerClass(Hu);var $y=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Hu(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};$y.className="LSTM";ae.registerClass($y);var l0=class extends zu{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){ey(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{$i(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Cr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ty(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}ny(t)}};l0.className="StackedRNNCells";ae.registerClass(l0);function es(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>y7(t(),n),i=()=>Ou(s,t,r);return!a||a<=1?qt(i().clone()):Array(a).fill(void 0).map(i).map(o=>qt(o.clone()))}var cne=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},kv=class extends Zr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Zt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ft(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new fa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ft(a)):this.states_=[Ft(a)];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ft(a)):this.states_[0]=Ft(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ie(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>qt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=Rr(l,r[0],a,s[0],i[0]),h=Rr(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};kv.className="ConvRNN2D";var h0=class extends Hu{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Yl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Yl(r||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=a||"valid",er(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Yl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=ht(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends pr{apply(c,h){let d=l.apply([u]),p=Vr([u]),f=l.apply([u*2]);return VA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,se,te)=>!se||!se[te]?Y:O(se[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=Bt(this.kernel.read(),i,g),[S,T,E,F]=this.useBias?Bt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,w,S,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,F,this.padding);let[P,W,V,U]=Bt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,P),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(ie(u,f)),X=this.recurrentActivation.apply(ie(c,m)),G=ie(O(X,s),O(H,this.activation.apply(ie(h,A)))),ee=O(this.recurrentActivation.apply(ie(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=cne(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=la(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Kr(a,n,this.dataFormat):a}recurrentConv(e,t){return la(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};h0.className="ConvLSTM2DCell";ae.registerClass(h0);var Dy=class extends kv{constructor(e){let t=new h0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Dy.className="ConvLSTM2D";ae.registerClass(Dy);var d0=class extends Ze{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Ou(()=>y7(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};d0.className="Dropout";ae.registerClass(d0);var Oy=class extends d0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Oy.className="SpatialDropout1D";ae.registerClass(Oy);var zy=class extends Ze{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=jt(e.kernelConstraint),this.biasConstraint=jt(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ht(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ht(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e),r=s7(this.activation.getClassName()),a;return r!=null?a=Xr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Xr(n,this.kernel.read()),this.bias!=null&&(a=Kr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ja(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Dense";ae.registerClass(zy);var Py=class extends Ze{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ht(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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z(()=>(e=Be(e),MQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="RepeatVector";ae.registerClass(Wy);var By=class extends Ze{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Ka(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let 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o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c<l+i;++c)u.push(c);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Yy=class extends Li{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Jy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e);return Ou(()=>Pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Jy.className="GaussianNoise";ae.registerClass(Jy);var Qy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.rate>0&&this.rate<1?Ou(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Pp(n.shape,1,r))},()=>n,t.training||!1):n})}};Qy.className="GaussianDropout";ae.registerClass(Qy);var eg=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Be(e),a=r.shape,s=a.length,i=Tr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ri(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,Tr(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return qu(r,A,y,g,w,this.epsilon)}else return 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e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};ng.className="LayerNormalization";ae.registerClass(ng);function fne(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Sr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=au(e,t,n,o):i=Yc(e,t,n,o),a==="channelsFirst"&&(i=st(i,[0,3,1,2])),i})}function Iv(e,t,n,r,a,s){return z(()=>{Rt(a),c7(s),er(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Sr()),s==null&&(s="max"),e=xv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Im(e,t,n,o):i=hm(e,t,n,o),a==="channelsFirst"&&(i=st(i,[0,4,1,2,3])),i})}var Sv=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,er(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=Rr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=$u(Be(e),2);let n=this.poolingFunction(Be(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ua(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ag=class extends Sv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),p0(e,t,n,r,a,"max")}};ag.className="MaxPooling1D";ae.registerClass(ag);var sg=class extends Sv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=Rr(t,this.poolSize[0],this.padding,this.strides[0]),n=Rr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ig=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),p0(e,t,n,r,a,"max")}};ig.className="MaxPooling2D";ae.registerClass(ig);var og=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),p0(e,t,n,r,a,"avg")}};og.className="AveragePooling2D";ae.registerClass(og);var Tv=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),er(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Rr(t,this.poolSize[0],this.padding,this.strides[0]),n=Rr(n,this.poolSize[1],this.padding,this.strides[1]),r=Rr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},lg=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),Iv(e,t,n,r,a,"max")}};lg.className="MaxPooling3D";ae.registerClass(lg);var cg=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),Iv(e,t,n,r,a,"avg")}};cg.className="AveragePooling3D";ae.registerClass(cg);var Ev=class extends Ze{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},ug=class extends Ev{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Be(e);return Nt(n,1)})}};ug.className="GlobalAveragePooling1D";ae.registerClass(ug);var hg=class extends Ev{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Be(e);return Nn(n,1)})}};hg.className="GlobalMaxPooling1D";ae.registerClass(hg);var Cv=class extends Ze{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},dg=class extends Cv{call(e,t){return z(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?Nt(n,[1,2]):Nt(n,[2,3])})}};dg.className="GlobalAveragePooling2D";ae.registerClass(dg);var pg=class extends Cv{call(e,t){return z(()=>{let n=Be(e);return 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r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Cr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Ane:e.mergeMode,mne(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Rn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=_v(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new 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i;return this.mergeMode==="concat"?i=VA([r,a]):this.mergeMode==="sum"?i=ie(r,a):this.mergeMode==="ave"?i=O(.5,ie(r,a)):this.mergeMode==="mul"?i=O(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){$i(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),$i(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;qr(w===0,"input layer has >1 nodes"),qr(b===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof ql))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function K7(e,t){return $te(e,t,"classWeight")}async function Z7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());Ie(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)v.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)v.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function Y7(e,t,n){if(n instanceof je)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function zte(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Lte(e,t,n){let r=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new zi(c),h=Wu(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Dte(f,a[p]));let m=Nt(f);t.push(m),p===0?d=f:d=ie(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=Nt(m(r[A],h[A]))}qt(f),s.push(f)}return d=Nt(d),this.calculateLosses().forEach(p=>{d=ie(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new zi(s),o=Wu(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Nt(c(a[l],o[l]));l===0?n=u:n=ie(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=Nt(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return Ute(this,e,t,n)}async fitDataset(e,t){return Lte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Ie(s),Rn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=_d().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-_d().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ma(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ma(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ma(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ma(r0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ma(r0(e)));{let e={};for(let t in this.metrics)e[t]=ma(r0(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Lu(e.optimizer_config),n=Cr(t),r;if(typeof e.loss=="string")r=Mi(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>Mi(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=Mi(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>Mi(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=Mi(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=kn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await kn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Xte,generatedBy:`TensorFlow.js tfjs-layers v${dy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await kn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=kn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;j7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){j7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Aa.className="Model";ae.registerClass(Aa);var av=class extends Aa{};av.className="Functional";ae.registerClass(av);async function Kte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Lu(n),a=Cr(r,t);if(e.weightsManifest!=null){let s=await kn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),Ie(s)}return a}async function Yte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=kn.getLoadHandlers(e,t);if(n.length===0)n.push(kn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Zte(e,void 0,t)}async function Zte(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=Cr(Lu(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Zl))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Cr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="ThresholdedReLU";ae.registerClass(Iy);var Sy=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new gy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Be(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Softmax";ae.registerClass(Sy);function Yl(e,t,n){if(typeof e=="number")return Ri(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(s==="channelsFirst"&&(e=st(e,[0,2,1])),a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Sd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Kr(o,n)),o})}function bv(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Sr()),Rt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Ny(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ha.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=st(l,[0,3,1,2])),l})}function ine(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Sr()),Rt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=wv(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Am(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Kr(o,n)),s==="channelsFirst"&&(o=st(o,[0,4,1,2,3])),o})}var Ty=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Ty.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yl(t.kernelSize,e,"kernelSize"),this.strides=Yl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,er(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Qa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ht(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=Yl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(qr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!OA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ja(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Uu=class extends Ty{constructor(e,t){super(e,t);this.kernel=null,Uu.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ht(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Be(e);let n,r=this.bias==null?null:this.bias.read(),a=i7(this.activation.getClassName());if(a!=null&&this.rank===2)n=bv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=sne(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=bv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=ine(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ht(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Rr(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Tt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Ut(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Hu=class extends Uu{constructor(e){super(2,e);Hu.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!OA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Hu.className="Conv2D";ae.registerClass(Hu);var i0=class extends Uu{constructor(e){super(3,e);i0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};i0.className="Conv3D";ae.registerClass(i0);var Ey=class extends Hu{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ht(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Be(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=s0(o,h,c,this.padding),f=s0(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=st(n,[0,2,3,1]));let A=Nd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=st(A,[0,3,1,2])),this.bias!=null&&(A=Kr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ht(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=s0(t[r],o,s,this.padding),t[a]=s0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ey.className="Conv2DTranspose";ae.registerClass(Ey);var _v=class extends Uu{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Ht(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Ht(t.pointwiseConstraint)}build(e){if(e=ht(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Be(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=st(e,[0,2,3,1])),n=$m(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Kr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=st(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};_v.className="SeparableConv";var Cy=class extends _v{constructor(e){super(2,e)}};Cy.className="SeparableConv2D";ae.registerClass(Cy);var o0=class extends Uu{constructor(e){super(1,e);o0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!OA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};o0.className="Conv1D";ae.registerClass(o0);var Ry=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Be(e),this.dataFormat==="channelsLast"){let n=zp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=zp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="Cropping2D";ae.registerClass(Ry);var My=class extends Ze{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,NQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Be(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=st(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return st(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};My.className="UpSampling2D";ae.registerClass(My);function one(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Sr()),Rt(a);let i=Ny(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=xl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=st(i,[0,3,1,2])),i})}var Fy=class extends Ty{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ht(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=ht(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Be(e);let n=one(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Kr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Rr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Rr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};Fy.className="DepthwiseConv2D";ae.registerClass(Fy);function vv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function kv(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Tr(2,l));if(t=st(t,c),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=tn(a,-1)),a=st(a,c)),r&&(t=Ln(t,0),a!=null&&(a=Ln(a,0)));let u=[],h,d=n,p=t.shape[0],f=ur(t),m;a!=null&&(m=ur(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=Pn(_).sub(_),S=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,F)=>w[1][F].mul(_).add(E.mul(x)));return{output:S,newStates:T}});h=b.output,d=b.newStates}o&&u.push(h)}let A;return o&&(A=pn(u,1)),[h,A,d]})}var Zr=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new l0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Tr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ny(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");ny(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new fa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ft([n,r])):this.states_=[Ft([n,this.cell.stateSize])];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ft([n,r])):this.states_[0]=Ft([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ie(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>qt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=vv(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Er){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Be(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=kv((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Ft(e.shape);return t=Me(t,[1,2]),t=$u(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?VA(t,[1,n]):t):this.cell.stateSize>1?[VA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Cr(r,n);return new e(Object.assign(t,{cell:a}))}};Zr.className="RNN";ae.registerClass(Zr);var zu=class extends Ze{},c0=class extends zu{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Gl([1,Za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Xr(O(e,s),this.kernel.read()):a=Xr(e,this.kernel.read()),this.bias!=null&&(a=Kr(a,this.bias.read())),i!=null&&(n=O(n,i));let o=ie(a,Xr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ja(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};c0.className="SimpleRNNCell";ae.registerClass(c0);var $y=class extends Zr{constructor(e){e.cell=new c0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};$y.className="SimpleRNN";ae.registerClass($y);var u0=class extends zu{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Gl([1,Za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ht(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let c=Xr(e,this.kernel.read());this.useBias&&(c=Kr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Bt(u,[2*this.units,this.units],u.rank-1),p=Xr(r,h),[f,m,A]=Bt(c,3,c.rank-1),[y,g]=Bt(p,2,p.rank-1);i=this.recurrentActivation.apply(ie(f,y)),o=this.recurrentActivation.apply(ie(m,g));let w=Xr(O(o,r),d);l=this.activation.apply(ie(A,w));let b=ie(O(i,r),O(ie(1,St(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ja(this.activation),recurrentActivation:Ja(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};u0.className="GRUCell";ae.registerClass(u0);var Dy=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new u0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Dy.className="GRU";ae.registerClass(Dy);var ju=class extends zu{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Qa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=Gl([1,Za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ht(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends pr{apply(i,o){let l=a.apply([s]),c=new Lp().apply([s]),u=a.apply([s*2]);return A7(A7(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Xr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=ie(h,Xr(r,this.recurrentKernel.read())),this.useBias&&(h=Kr(h,this.bias.read()));let[d,p,f,m]=Bt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=ie(O(l,a),O(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=O(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ja(this.activation),recurrentActivation:Ja(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};ju.className="LSTMCell";ae.registerClass(ju);var Oy=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new ju(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Oy.className="LSTM";ae.registerClass(Oy);var l0=class extends zu{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){ny(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{$i(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Cr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ry(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}ay(t)}};l0.className="StackedRNNCells";ae.registerClass(l0);function es(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>g7(t(),n),i=()=>Ou(s,t,r);return!a||a<=1?qt(i().clone()):Array(a).fill(void 0).map(i).map(o=>qt(o.clone()))}var lne=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},Iv=class extends Zr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Zt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ft(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new fa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ft(a)):this.states_=[Ft(a)];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ft(a)):this.states_[0]=Ft(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ie(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>qt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Rr(l,r[0],a,s[0],i[0]),h=Rr(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};Iv.className="ConvRNN2D";var h0=class extends ju{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Yl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Yl(r||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=a||"valid",er(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Yl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=ht(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends pr{apply(u,h){let d=l.apply([c]),p=Vr([c]),f=l.apply([c*2]);return HA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=es({ones:()=>Pn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,se,te)=>!se||!se[te]?Y:O(se[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=es({ones:()=>Pn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=Bt(this.kernel.read(),i,g),[S,T,E,F]=this.useBias?Bt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,S,this.padding),u=this.inputConv(u,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,F,this.padding);let[P,W,V,U]=Bt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,P),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(ie(c,f)),X=this.recurrentActivation.apply(ie(u,m)),G=ie(O(X,s),O(H,this.activation.apply(ie(h,A)))),ee=O(this.recurrentActivation.apply(ie(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=lne(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=la(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Kr(a,n,this.dataFormat):a}recurrentConv(e,t){return la(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};h0.className="ConvLSTM2DCell";ae.registerClass(h0);var zy=class extends Iv{constructor(e){let t=new h0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};zy.className="ConvLSTM2D";ae.registerClass(zy);var d0=class extends Ze{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Ou(()=>g7(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};d0.className="Dropout";ae.registerClass(d0);var Py=class extends d0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Py.className="SpatialDropout1D";ae.registerClass(Py);var Ly=class extends Ze{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=Qa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ht(e.kernelConstraint),this.biasConstraint=Ht(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ht(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ht(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e),r=i7(this.activation.getClassName()),a;return r!=null?a=Xr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Xr(n,this.kernel.read()),this.bias!=null&&(a=Kr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ja(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="Dense";ae.registerClass(Ly);var Wy=class extends Ze{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ht(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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z(()=>(e=Be(e),RQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="RepeatVector";ae.registerClass(Vy);var Uy=class extends Ze{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Ka(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let 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eg=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e);return Ou(()=>Pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};eg.className="GaussianNoise";ae.registerClass(eg);var tg=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.rate>0&&this.rate<1?Ou(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Pp(n.shape,1,r))},()=>n,t.training||!1):n})}};tg.className="GaussianDropout";ae.registerClass(tg);var ng=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Xa(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Be(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=Od(n,this.axis,s),l=Ri(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),qu(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};ag.className="LayerNormalization";ae.registerClass(ag);function pne(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Sr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=au(e,t,n,o):i=Yc(e,t,n,o),a==="channelsFirst"&&(i=st(i,[0,3,1,2])),i})}function Sv(e,t,n,r,a,s){return z(()=>{Rt(a),u7(s),er(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Sr()),s==null&&(s="max"),e=wv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Nm(e,t,n,o):i=pm(e,t,n,o),a==="channelsFirst"&&(i=st(i,[0,4,1,2,3])),i})}var Nv=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,er(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=ht(e);let t=Rr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=$u(Be(e),2);let n=this.poolingFunction(Be(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ua(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ig=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),p0(e,t,n,r,a,"max")}};ig.className="MaxPooling1D";ae.registerClass(ig);var og=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=Rr(t,this.poolSize[0],this.padding,this.strides[0]),n=Rr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},lg=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),p0(e,t,n,r,a,"max")}};lg.className="MaxPooling2D";ae.registerClass(lg);var cg=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),p0(e,t,n,r,a,"avg")}};cg.className="AveragePooling2D";ae.registerClass(cg);var Ev=class extends Ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),er(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=ht(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Rr(t,this.poolSize[0],this.padding,this.strides[0]),n=Rr(n,this.poolSize[1],this.padding,this.strides[1]),r=Rr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ug=class extends Ev{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),Sv(e,t,n,r,a,"max")}};ug.className="MaxPooling3D";ae.registerClass(ug);var hg=class extends Ev{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),er(r),Sv(e,t,n,r,a,"avg")}};hg.className="AveragePooling3D";ae.registerClass(hg);var Cv=class extends Ze{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},dg=class extends Cv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Be(e);return Nt(n,1)})}};dg.className="GlobalAveragePooling1D";ae.registerClass(dg);var pg=class extends Cv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Be(e);return Nn(n,1)})}};pg.className="GlobalMaxPooling1D";ae.registerClass(pg);var Rv=class extends Ze{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},fg=class extends Rv{call(e,t){return z(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?Nt(n,[1,2]):Nt(n,[2,3])})}};fg.className="GlobalAveragePooling2D";ae.registerClass(fg);var mg=class extends Rv{call(e,t){return z(()=>{let n=Be(e);return 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r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Cr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?mne:e.mergeMode,fne(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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i;return this.mergeMode==="concat"?i=HA([r,a]):this.mergeMode==="sum"?i=ie(r,a):this.mergeMode==="ave"?i=O(.5,ie(r,a)):this.mergeMode==="mul"?i=O(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){$i(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),$i(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ya(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Fn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Fn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=jn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=jn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=jn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},$re=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Dre="?tfjs-format=file",Ore="model.json",p6=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new $re}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=kn.browserHTTPRequest(e,this.loadOptions);else{let t=kn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(kn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=kn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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Mae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Fae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],$ae=[33,133,362,263,1,78,308],Dhe=Mae.map(e=>n2[e]),Ohe=Fae.map(e=>n2[e]),zhe=$ae.map(e=>n2[e]);var r2=ea.leftEyeLower0,a2=ea.rightEyeLower0,nc={leftBounds:[r2[0],r2[r2.length-1]],rightBounds:[a2[0],a2[a2.length-1]]},$0={count:468,mouth:13,symmetryLine:[13,ea.midwayBetweenEyes[0]]},G6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},rc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function D0(e,t,n,r){for(let a=0;a<t2.length;a++){let{key:s,indices:i}=t2[a],o=ea[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var s2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Yu({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?F0(r,[0,0]):M0,l=r!==0?i.map(h=>[...H6(h,o),h[2]]):i,u=r!==0?j6(a):M0,c=[...ec({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+as(c,u[0]),h[1]+as(c,u[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[nc.leftBounds[0]][2],r=t[nc.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=R0(C0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Yu(i),l=We.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&wr.flags.IS_BROWSER&&(l=We.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<rc.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(rc.index)}}getAdjustedIrisCoords(t,n,r){let a=t[ea[`${r}EyeUpper0`][rc.upperCenter]][2],s=t[ea[`${r}EyeLower0`][rc.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=a:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=B6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=C0(o),u=R0(l),c=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:h,landmarks:c}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,u,c=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&wr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=$0.count?$0.symmetryLine:G6.symmetryLine;c=e2(i.landmarks[_],i.landmarks[x]);let S=ec({startPoint:i.startPoint,endPoint:i.endPoint}),T=[S[0]/t.shape[2],S[1]/t.shape[1]],E=We.rotateWithOffset(t,c,0,T);h=F0(-c,S),n.face.mesh.enabled?u=tc({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):u=tc({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=M0;let _=t.clone();n.face.mesh.enabled?u=tc({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):u=tc({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:u};let[,d,p]=this.meshDetector.predict(u),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=j(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:S}=this.getEyeBox(A,u,nc.leftBounds[0],nc.leftBounds[1],!0),{box:T,boxSize:E,crop:F}=this.getEyeBox(A,u,nc.rightBounds[0],nc.rightBounds[1]),W=this.irisModel.predict(it([S,F])).dataSync(),V=W.slice(0,rc.numCoordinates*3),{rawCoords:U,iris:H}=this.getEyeCoords(V,_,x,!0),X=W.slice(rc.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,E),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(D0(A,U,"left",null),D0(A,G,"right",null)):Y<1?D0(A,U,"left",["EyeUpper0","EyeLower0"]):D0(A,G,"right",["EyeUpper0","EyeLower0"]);let se=this.getAdjustedIrisCoords(A,H,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(se).concat(te)}let y=this.transformRawCoords(A,i,c,h);i=C0(this.calculateLandmarksBoundingBox(y),1.5);let g=Tn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&wr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=$0.count?$0.symmetryLine:G6.symmetryLine;c=e2(i.landmarks[_],i.landmarks[x]);let S=ec({startPoint:i.startPoint,endPoint:i.endPoint}),T=[S[0]/t.shape[2],S[1]/t.shape[1]],E=We.rotateWithOffset(t,c,0,T);h=F0(-c,S),u=tc({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:u,rawCoords:A},b=R0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var o2=class{constructor(t,n,r,a){this.facePipeline=new s2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(ea))l[h]=ea[h].map(d=>i[d]);let u=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,c=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:u,boxRaw:c,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Bi=[null,null,null];async function l2(e){Bi=await Promise.all([!Bi[0]&&e.face.enabled?W6(e):null,!Bi[1]&&e.face.mesh.enabled?pt(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Bi[2]&&e.face.iris.enabled?pt(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new o2(Bi[0],Bi[1],Bi[2],e);return e.face.mesh.enabled&&e.debug&&Ee(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ee(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var Dae=Wi;var g2={};xr(g2,{PoseNet:()=>x2,load:()=>w2});function Oae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var c2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Oae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function u2(e){return Math.floor(e/2)}var h2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return 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ac=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],sc=ac.length,Ju=ac.reduce((e,t,n)=>(e[t]=n,e),{}),Pae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],ede=Pae.map(([e,t])=>[Ju[e],Ju[t]]),X6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function d2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+sc)}}function O0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=d2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function p2(e,t,n){return e<t?t:e>n?n:e}function K6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function f2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function Z6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function Lae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+sc)}}function Wae(e,t){let n=[];for(let r=0;r<sc;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=Lae(a,s,r,t);n.push(o),n.push(i)}return Tn(n,[sc,2])}function Y6(e,t,n){return z(()=>e.toTensor().mul(be(t,"int32")).toFloat().add(Wae(e,n)))}function Bae(e,t){return z(()=>{let n=e.div(be(t,"int32"));return e.sub(n.mul(be(t,"int32")))})}function J6(e){let[t,n,r]=e.shape;return z(()=>{let 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r=0,a=J6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],u=Y6(l,Vae,o),c=await u.buffer(),d=Array.from(Z6(i,l)).map((f,m)=>(r+=f,{position:{y:c.get(m,0),x:c.get(m,1)},part:ac[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),u.dispose(),{keypoints:p,score:r/d.length}}var jae=1,a4=16;function s4(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return K6(r,n,i.y,i.x)<=t})}function Hae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(s4(e,t,s,o)||(a+=i),a),0)/n.length}function i4(e,t,n,r,a,s,i){let o=[],l=q6(i,jae,e),u=a^2;for(;o.length<s&&!l.empty();){let c=l.dequeue(),h=O0(c.part,a4,t);if(s4(o,u,h,c.part.id))continue;let d=n4(c,e,t,a4,n,r),p=Hae(o,u,d);p>i&&o.push({keypoints:d,score:p})}return o}async function o4(e){return Promise.all(e.map(t=>t.buffer()))}function 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r=l4(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Xae(t,a,n,this.inputSize):await qae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function w2(e){let t=await pt(e.body.modelPath),n=new c2(t);return e.debug&&Ee(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new x2(n)}var I2={};xr(I2,{HandPose:()=>N2,load:()=>T2});function z0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Qu(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function c4(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return We.cropAndResize(t,s,[0],n)}function u4(e,t){let 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g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=j(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let S={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(S)}else this.storedBoxes[i]=null;y.dispose()}else{let l=P0(L0(o),m4),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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g4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],x4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Ar;async function C2(e){return Ar||(Ar=await pt(e.body.modelPath),Ar.width=parseInt(Ar.signature.inputs["input_1:0"].tensorShape.dim[2].size),Ar.height=parseInt(Ar.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ee(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),Ar}async function R2(e,t){if(!Ar||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=We.resizeBilinear(e,[Ar.width,Ar.height],!1),a=ge(r,[255]);r.dispose();let s;if(t.profile){let u=await cn(()=>Ar.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),gn("blazepose",u)}else{let u=await Ar.predict(a);s=u.find(c=>c.size===195||c.size===155).dataSync(),u.forEach(c=>c.dispose())}a.dispose();let i=[],o=s.length===195?g4:x4,l=5;for(let 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},C6=class extends Yt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new C6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),vr(n,t)}},R6=class extends Yt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=un([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=Tn([s,a,o,i],[1,4])}else this.cropBox=Tn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new R6(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=pl.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=tn(we(e,"float32"),0),n;n=We.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return j(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},M6=class{},F6=class extends Yt{split(e){return new mae(this,e)}},mae=class extends F6{constructor(e,t){super();this.upstream=e,this.impl=new Aae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Aae=class extends Dg{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},gae=class extends Yt{decodeUTF8(){return new yae(this)}},yae=class extends F6{constructor(e){super();this.upstream=e,this.impl=new xae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},xae=class extends Dg{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ak();this.decoder=new 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Rae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Mae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Fae=[33,133,362,263,1,78,308],$he=Rae.map(e=>r2[e]),Dhe=Mae.map(e=>r2[e]),Ohe=Fae.map(e=>r2[e]);var a2=ea.leftEyeLower0,s2=ea.rightEyeLower0,nc={leftBounds:[a2[0],a2[a2.length-1]],rightBounds:[s2[0],s2[s2.length-1]]},D0={count:468,mouth:13,symmetryLine:[13,ea.midwayBetweenEyes[0]]},q6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},rc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function O0(e,t,n,r){for(let a=0;a<n2.length;a++){let{key:s,indices:i}=n2[a],o=ea[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var i2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Yu({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?$0(r,[0,0]):F0,l=r!==0?i.map(h=>[...G6(h,o),h[2]]):i,c=r!==0?j6(a):F0,u=[...ec({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+as(u,c[0]),h[1]+as(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[nc.leftBounds[0]][2],r=t[nc.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=M0(R0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Yu(i),l=We.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&wr.flags.IS_BROWSER&&(l=We.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<rc.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(rc.index)}}getAdjustedIrisCoords(t,n,r){let a=t[ea[`${r}EyeUpper0`][rc.upperCenter]][2],s=t[ea[`${r}EyeLower0`][rc.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=V6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=R0(o),c=M0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&wr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=D0.count?D0.symmetryLine:q6.symmetryLine;u=t2(i.landmarks[_],i.landmarks[x]);let S=ec({startPoint:i.startPoint,endPoint:i.endPoint}),T=[S[0]/t.shape[2],S[1]/t.shape[1]],E=We.rotateWithOffset(t,u,0,T);h=$0(-u,S),n.face.mesh.enabled?c=tc({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=tc({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=F0;let _=t.clone();n.face.mesh.enabled?c=tc({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):c=tc({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=j(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:S}=this.getEyeBox(A,c,nc.leftBounds[0],nc.leftBounds[1],!0),{box:T,boxSize:E,crop:F}=this.getEyeBox(A,c,nc.rightBounds[0],nc.rightBounds[1]),W=this.irisModel.predict(it([S,F])).dataSync(),V=W.slice(0,rc.numCoordinates*3),{rawCoords:U,iris:H}=this.getEyeCoords(V,_,x,!0),X=W.slice(rc.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,E),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(O0(A,U,"left",null),O0(A,G,"right",null)):Y<1?O0(A,U,"left",["EyeUpper0","EyeLower0"]):O0(A,G,"right",["EyeUpper0","EyeLower0"]);let se=this.getAdjustedIrisCoords(A,H,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(se).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=R0(this.calculateLandmarksBoundingBox(y),1.5);let g=Tn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&wr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=D0.count?D0.symmetryLine:q6.symmetryLine;u=t2(i.landmarks[_],i.landmarks[x]);let S=ec({startPoint:i.startPoint,endPoint:i.endPoint}),T=[S[0]/t.shape[2],S[1]/t.shape[1]],E=We.rotateWithOffset(t,u,0,T);h=$0(-u,S),c=tc({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},b=M0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var l2=class{constructor(t,n,r,a){this.facePipeline=new i2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(ea))l[h]=ea[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Bi=[null,null,null];async function c2(e){Bi=await Promise.all([!Bi[0]&&e.face.enabled?B6(e):null,!Bi[1]&&e.face.mesh.enabled?pt(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Bi[2]&&e.face.iris.enabled?pt(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new l2(Bi[0],Bi[1],Bi[2],e);return e.face.mesh.enabled&&e.debug&&Ee(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ee(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var $ae=Wi;var x2={};xr(x2,{PoseNet:()=>w2,load:()=>b2});function Dae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var u2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Dae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function h2(e){return Math.floor(e/2)}var d2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return 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p2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+sc)}}function z0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=p2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function f2(e,t,n){return e<t?t:e>n?n:e}function Z6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function m2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function Y6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function Pae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+sc)}}function Lae(e,t){let n=[];for(let r=0;r<sc;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=Pae(a,s,r,t);n.push(o),n.push(i)}return Tn(n,[sc,2])}function J6(e,t,n){return z(()=>e.toTensor().mul(be(t,"int32")).toFloat().add(Lae(e,n)))}function Wae(e,t){return z(()=>{let n=e.div(be(t,"int32"));return e.sub(n.mul(be(t,"int32")))})}function Q6(e){let[t,n,r]=e.shape;return z(()=>{let 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r=0,a=Q6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=J6(l,Bae,o),u=await c.buffer(),d=Array.from(Y6(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:ac[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var Uae=1,s4=16;function i4(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return Z6(r,n,i.y,i.x)<=t})}function Hae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(i4(e,t,s,o)||(a+=i),a),0)/n.length}function o4(e,t,n,r,a,s,i){let o=[],l=X6(i,Uae,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=z0(u.part,s4,t);if(i4(o,c,h,u.part.id))continue;let d=r4(u,e,t,s4,n,r),p=Hae(o,c,d);p>i&&o.push({keypoints:d,score:p})}return o}async function l4(e){return Promise.all(e.map(t=>t.buffer()))}function 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g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=j(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let S={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(S)}else this.storedBoxes[i]=null;y.dispose()}else{let l=L0(W0(o),A4),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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x4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],w4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Ar;async function R2(e){return Ar||(Ar=await pt(e.body.modelPath),Ar.width=parseInt(Ar.signature.inputs["input_1:0"].tensorShape.dim[2].size),Ar.height=parseInt(Ar.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ee(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),Ar}async function M2(e,t){if(!Ar||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=We.resizeBilinear(e,[Ar.width,Ar.height],!1),a=ge(r,[255]);r.dispose();let s;if(t.profile){let c=await cn(()=>Ar.predict(a));s=c.result.find(u=>u.size===195||u.size===155).dataSync(),c.result.forEach(u=>u.dispose()),gn("blazepose",c)}else{let c=await Ar.predict(a);s=c.find(u=>u.size===195||u.size===155).dataSync(),c.forEach(u=>u.dispose())}a.dispose();let i=[],o=s.length===195?x4:w4,l=5;for(let 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s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}}}}}var is=[];async function E4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!is[r]&&oe.bufferedOutput&&(is[r]={...t[r]}),n.strokeStyle=oe.color,n.lineWidth=oe.lineWidth,oe.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=oe.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:oe.color,oe.bufferedOutput?(is[r].keypoints[a][0]=(is[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,is[r].keypoints[a][1]=(is[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,H0(n,is[r].keypoints[a][0],is[r].keypoints[a][1])):H0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(oe.drawLabels&&(n.font=oe.font,t[r].keypoints))for(let a of t[r].keypoints)n.fillStyle=oe.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:oe.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4);if(oe.drawPolygons&&t[r].keypoints){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&W2(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),G0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),G0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),G0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),G0(n,s)}}}}async function C4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t){if(oe.drawBoxes&&(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?ic(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):ic(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels&&(oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])),n.stroke()),oe.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=oe.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:oe.color,H0(n,a[0],a[1]);if(oe.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=oe.lineWidth,n.beginPath(),n.strokeStyle=oe.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:oe.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function R4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t)if(oe.drawBoxes){if(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?ic(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):ic(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])}n.stroke()}}}async function ase(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function sse(e,t){!t||!e||e instanceof HTMLCanvasElement&&(T4(e,t.face),E4(e,t.body),C4(e,t.hand),N4(e,t.gesture),R4(e,t.object))}var q0=`
+`)}var G0=2048,Mt=null,sn=null,Dt=null;function L2(e,t){let n;if(e instanceof je)n=zr(e);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>G0&&(i=G0,o=i*s/a),o>G0&&(o=G0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)return Ee("Human: invalid input",e),{tensor:null,canvas:null};(!Mt||Mt.width!==i||Mt.height!==o)&&(Mt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Mt.width!==i&&(Mt.width=i),Mt.height!==o&&(Mt.height=o));let l=Mt.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Mt.width,Mt.height),t.filter.enabled){if((!Dt||!sn||Mt.width!==sn.width||Mt.height!==sn.height)&&(sn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Mt.width,Mt.height):document.createElement("canvas"),sn.width!==Mt.width&&(sn.width=Mt.width),sn.height!==Mt.height&&(sn.height=Mt.height),Dt=wr.flags.IS_BROWSER?new I4({canvas:sn}):null),!Dt)return{tensor:null,canvas:Mt};Dt.reset(),Dt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Dt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Dt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Dt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Dt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Dt.addFilter("hue",t.filter.hue),t.filter.negative&&Dt.addFilter("negative"),t.filter.sepia&&Dt.addFilter("sepia"),t.filter.vintage&&Dt.addFilter("brownie"),t.filter.sepia&&Dt.addFilter("sepia"),t.filter.kodachrome&&Dt.addFilter("kodachrome"),t.filter.technicolor&&Dt.addFilter("technicolor"),t.filter.polaroid&&Dt.addFilter("polaroid"),t.filter.pixelate!==0&&Dt.addFilter("pixelate",t.filter.pixelate),Dt.apply(Mt)}else sn=Mt,Dt&&(Dt=null);let c;if(sn.data){let h=[sn.height,sn.width,3];c=wd(sn.data,h,"int32")}else if(t.backend==="webgl"||sn instanceof ImageData)c=pl.fromPixels(sn);else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(sn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=pl.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?sn:null;return{tensor:n,canvas:r}}var W2={};xr(W2,{all:()=>ase,body:()=>T4,canvas:()=>rse,drawOptions:()=>oe,face:()=>N4,gesture:()=>S4,hand:()=>E4,object:()=>C4});var At={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var oe={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function q0(e,t,n,r=null){e.fillStyle=oe.useDepth&&r?`rgba(${127.5+2*(r||0)}, ${127.5-2*(r||0)}, 255, 0.3)`:oe.color,e.beginPath(),e.arc(t,n,oe.pointSize,0,2*Math.PI),e.fill()}function ic(e,t,n,r,a){if(e.beginPath(),oe.useCurves){let s=(t+t+r)/2,i=(n+n+a)/2;e.ellipse(s,i,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=oe.lineWidth,e.moveTo(t+oe.roundRect,n),e.lineTo(t+r-oe.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+oe.roundRect),e.lineTo(t+r,n+a-oe.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-oe.roundRect,n+a),e.lineTo(t+oe.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-oe.roundRect),e.lineTo(t,n+oe.roundRect),e.quadraticCurveTo(t,n,t+oe.roundRect,n),e.closePath();e.stroke()}function B2(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=oe.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:oe.color,e.fillStyle=oe.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:oe.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),oe.fillPolygons&&(e.closePath(),e.fill())}}function X0(e,t=[]){if(!(t===void 0||t.length===0)){if(!oe.useCurves||t.length<=2){B2(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,a=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),oe.fillPolygons&&(e.closePath(),e.fill())}}async function S4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=oe.font,n.fillStyle=oe.color;let r=1;for(let a=0;a<t.length;a++){let s=[],i=[];if([s,i]=Object.entries(t[a]),i.length>1&&i[1].length>0){let o=s[1]>0?`#${s[1]}`:"",l=`${s[0]} ${o}: ${i[1]}`;oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(l,8,2+r*oe.lineHeight)),n.fillStyle=oe.labelColor,n.fillText(l,6,0+r*oe.lineHeight),r+=1}}}async function N4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let r of t){n.font=oe.font,n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.drawBoxes&&(oe.useRawBoxes?ic(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):ic(n,r.box[0],r.box[1],r.box[2],r.box[3]));let a=[];if(a.push(`face confidence: ${Math.trunc(100*r.confidence)}%`),r.genderConfidence&&a.push(`${r.gender||""} ${Math.trunc(100*r.genderConfidence)}% confident`),r.age&&a.push(`age: ${r.age||""}`),r.iris&&a.push(`iris distance: ${r.iris}`),r.emotion&&r.emotion.length>0){let s=r.emotion.map(i=>`${Math.trunc(100*i.score)}% ${i.emotion}`);a.push(s.join(" "))}r.angle&&r.angle.roll&&a.push(`roll: ${Math.trunc(100*r.angle.roll)/100} yaw:${Math.trunc(100*r.angle.yaw)/100} pitch:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=oe.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*oe.lineHeight+r.box[1];oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=oe.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh&&r.mesh.length>0){if(oe.drawPoints)for(let s of r.mesh)q0(n,s[0],s[1],s[2]);if(oe.drawPolygons){n.lineWidth=1;for(let s=0;s<Wi.length/3;s++){let i=[Wi[s*3+0],Wi[s*3+1],Wi[s*3+2]].map(o=>r.mesh[o]);B2(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}}}}}var is=[];async function T4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!is[r]&&oe.bufferedOutput&&(is[r]={...t[r]}),n.strokeStyle=oe.color,n.lineWidth=oe.lineWidth,oe.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=oe.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:oe.color,oe.bufferedOutput?(is[r].keypoints[a][0]=(is[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,is[r].keypoints[a][1]=(is[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,q0(n,is[r].keypoints[a][0],is[r].keypoints[a][1])):q0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(oe.drawLabels&&(n.font=oe.font,t[r].keypoints))for(let a of t[r].keypoints)n.fillStyle=oe.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:oe.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4);if(oe.drawPolygons&&t[r].keypoints){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&B2(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),X0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),X0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),X0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>At.body.scoreThreshold&&s.push([a.position.x,a.position.y]),X0(n,s)}}}}async function E4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t){if(oe.drawBoxes&&(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?ic(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):ic(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels&&(oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])),n.stroke()),oe.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=oe.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:oe.color,q0(n,a[0],a[1]);if(oe.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=oe.lineWidth,n.beginPath(),n.strokeStyle=oe.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:oe.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function C4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t)if(oe.drawBoxes){if(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?ic(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):ic(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])}n.stroke()}}}async function rse(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function ase(e,t){!t||!e||e instanceof HTMLCanvasElement&&(N4(e,t.face),T4(e,t.body),E4(e,t.hand),S4(e,t.gesture),C4(e,t.object))}var K0=`
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-2Q==`;var M4="1.2.5";var oc,eh,th,Vi,K0,nh,Z0,Y0,J0,B2=class{constructor(t={}){oc.set(this,void 0);eh.set(this,void 0);th.set(this,void 0);Vi.set(this,void 0);this.analyze=(...t)=>{if(!ar(this,eh))return;let n=this.tf.engine().state.numTensors,r=ar(this,oc);hs(this,oc,n);let a=n-r;a!==0&&Ee(...t,a)};K0.set(this,t=>{if(!ar(this,th))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof He))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});nh.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=et();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&Ee("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ee("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ee(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ee("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&D6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ee("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ee("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ee(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(et()-n)}});Z0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(q0);break;case"full":n=await t(X0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});Y0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+q0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+X0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));J0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(q0):t(X0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=Eh,this.draw=L2,this.version=M4,this.config=no(At,t),this.state="idle",hs(this,oc,0),hs(this,eh,!1),hs(this,th,!1),hs(this,Vi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>P2(n,this.config),this.classes={facemesh:i2,age:Og,gender:Pg,emotion:Ug,faceres:Kg,body:this.config.body.modelPath.includes("posenet")?g2:E2,hand:I2,nanodet:$2},this.sysinfo=t5()}profileData(){return this.config.profile?Dg:{}}similarity(t,n){return this.config.face.description.enabled?Yg(t,n):this.config.face.embedding.enabled?O6(t,n):0}enhance(t){return Jg(t)}match(t,n,r=0){return z6(t,n,r)}async load(t={}){this.state="load";let n=et();t&&(this.config=no(this.config,t)),ar(this,Vi)&&(this.config.debug&&Ee(`version: ${this.version}`),this.config.debug&&Ee(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ee("platform:",this.sysinfo.platform),this.config.debug&&Ee("agent:",this.sysinfo.agent),await ar(this,nh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ee("configuration:",this.config),this.config.debug&&Ee("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?l2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?zg(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Vg(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Gg(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?qg(this.config):null),this.models.handpose||(this.config.hand.enabled?T2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?w2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?C2(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?M2(this.config):null),this.models.nanodet||(this.config.object.enabled?O2(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Zg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await l2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await zg(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Vg(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Gg(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await qg(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await T2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await w2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await C2(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await M2(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await O2(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Zg(this.config))),ar(this,Vi)&&(this.config.debug&&Ee("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),hs(this,Vi,!1));let r=Math.trunc(et()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var m,A,y,g;this.state="config";let a;this.config=no(this.config,n),this.state="check";let s=ar(this,K0).call(this,t);s&&(Ee(s,t),r({error:s}));let i=et();await ar(this,nh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=et();let o=P2(t,this.config);if(!o||!o.tensor){Ee("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(et()-a),this.analyze("Get Image:");let l,u,c,h,d;this.config.async?(c=this.config.face.enabled?Qg(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=et(),c=this.config.face.enabled?await Qg(this,o.tensor):[],d=Math.trunc(et()-a),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?R2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?F2(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=et(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await R2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?await F2(o.tensor,this.config):[]),d=Math.trunc(et()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=et(),u=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(et()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?z2(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=et(),h=this.config.object.enabled?await z2(o.tensor,this.config):[],d=Math.trunc(et()-a),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([c,l,u,h]=await Promise.all([c,l,u,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(a=et(),p=[...v4(c),..._4(l),...I4(u),...k4(c)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(et()-a)),this.perf.total=Math.trunc(et()-i),this.state="idle";let f={face:c,body:l,hand:u,gesture:p,object:h,performance:this.perf,canvas:o.canvas};r(f)})}async warmup(t={}){let n=et();t&&(this.config=no(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ar(this,Z0).call(this):typeof Image!="undefined"?a=await ar(this,Y0).call(this):a=await ar(this,J0).call(this),this.config.videoOptimized=r;let s=et();return this.config.debug&&Ee("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};oc=new WeakMap,eh=new WeakMap,th=new WeakMap,Vi=new WeakMap,K0=new WeakMap,nh=new WeakMap,Z0=new WeakMap,Y0=new WeakMap,J0=new WeakMap;var rh=0,F4=!1,_t={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function ise(){if(F4)return;let e=`
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this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ee(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(et()-n)}});J0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(K0);break;case"full":n=await t(Z0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});Q0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+K0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+Z0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));e1.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(K0):t(Z0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=Eh,this.draw=W2,this.version=R4,this.config=no(At,t),this.state="idle",hs(this,oc,0),hs(this,eh,!1),hs(this,th,!1),hs(this,Vi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>L2(n,this.config),this.classes={facemesh:o2,age:zg,gender:Lg,emotion:Hg,faceres:Zg,body:this.config.body.modelPath.includes("posenet")?x2:C2,hand:S2,nanodet:D2},this.sysinfo=n5()}profileData(){return this.config.profile?w0:{}}similarity(t,n){return this.config.face.description.enabled?Jg(t,n):this.config.face.embedding.enabled?z6(t,n):0}enhance(t){return Qg(t)}match(t,n,r=0){return P6(t,n,r)}async load(t={}){this.state="load";let n=et();t&&(this.config=no(this.config,t)),ar(this,Vi)&&(this.config.debug&&Ee(`version: ${this.version}`),this.config.debug&&Ee(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ee("platform:",this.sysinfo.platform),this.config.debug&&Ee("agent:",this.sysinfo.agent),await ar(this,nh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ee("configuration:",this.config),this.config.debug&&Ee("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?c2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Pg(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Ug(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?qg(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Xg(this.config):null),this.models.handpose||(this.config.hand.enabled?E2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?b2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?R2(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?F2(this.config):null),this.models.nanodet||(this.config.object.enabled?z2(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Yg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await 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this.perf.body):(this.state="run:body",a=et(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await M2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?await $2(o.tensor,this.config):[]),d=Math.trunc(et()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(c=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=et(),c=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(et()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start 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o=this.cpuAccums[i]/r*100,l=this.gpuAccums[i]/r*100,c=performance&&performance.memory?performance.memory.usedJSHeapSize/(1<<20):0;this.paramLogger(i,o,l,c,s,r,a),this.cpuAccums[i]=0,Promise.all(this.finished).then(()=>{this.gpuAccums[i]=0,this.finished=[]})}this.paramFrame=this.frameId,this.paramTime=n}}if(!this.detected||!this.chartFrame)this.chartFrame=this.frameId,this.chartTime=n,this.circularId=0;else{let r=n-this.chartTime,a=this.chartHz*r/1e3;for(;--a>0&&this.detected;){let i=(this.frameId-this.chartFrame)/r*1e3;this.chart[this.circularId%this.chartLen]=i;for(let o=0;o<this.names.length;o++)this.chartLogger(o,this.chart,this.circularId);this.circularId++,this.chartFrame=this.frameId,this.chartTime=n}}}begin(t){this.updateAccums(t)}end(t){this.updateAccums(t)}updateAccums(t){let n=this.names.indexOf(t);n===-1&&(n=this.names.length,this.addUI(t));let r=this.now(),a=r-this.t0;for(let 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lse(...e){if(!Array.isArray(e))return e;let t="";for(let n of e)typeof n=="object"?t+=JSON.stringify(n).replace(/{|}|"|\[|\]/g,"").replace(/,/g,", "):t+=n;return t}function $n(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;he.console&&console.log(n,...e)}function tr(e){let t=document.getElementById("status");t&&(t.innerText=e)}var wa={enabled:!1,original:null};async function cse(e){var n,r,a,s,i,o;if(document.getElementById("compare-container").style.display=wa.enabled?"block":"none",!wa.enabled||!(((n=e==null?void 0:e.face)==null?void 0:n.length)>0)||((a=(r=e==null?void 0:e.face[0])==null?void 0:r.embedding)==null?void 0:a.length)<=64)return;if(!wa.original)if(wa.original=e,$n("setting face compare baseline:",e.face[0]),e.face[0].tensor){let l=re.enhance(e.face[0]);if(l){let 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r=n.getContext("2d");r.fillStyle=he.baseBackground,r.fillRect(0,0,n.width,n.height),t.canvas?(t.canvas.width!==n.width&&(n.width=t.canvas.width),t.canvas.height!==n.height&&(n.height=t.canvas.height),r.drawImage(t.canvas,0,0,t.canvas.width,t.canvas.height,0,0,t.canvas.width,t.canvas.height)):r.drawImage(e,0,0,e.width,e.height,0,0,n.width,n.height),re.draw.face(n,t.face),re.draw.body(n,t.body),re.draw.hand(n,t.hand),re.draw.object(n,t.object),re.draw.gesture(n,t.gesture),await cse(t);let a=re.tf.engine(),s=a.backendInstance?`gpu: ${(a.backendInstance.numBytesInGPU?a.backendInstance.numBytesInGPU:0).toLocaleString()} bytes`:"",i=`system: ${a.state.numBytes.toLocaleString()} bytes ${s} | tensors: ${a.state.numTensors.toLocaleString()}`,o=t.canvas?`processing: ${t.canvas.width} x ${t.canvas.height}`:"",l=Math.trunc(10*he.detectFPS.reduce((h,d)=>h+d,0)/he.detectFPS.length)/10,c=Math.trunc(10*he.drawFPS.reduce((h,d)=>h+d,0)/he.drawFPS.length)/10,u=he.detectFPS.length>5&&l<5?'<font color="lightcoral">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>':"";document.getElementById("log").innerHTML=`
     video: ${he.camera.name} | facing: ${he.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${he.camera.width} x ${he.camera.height} ${o}<br>
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-    performance: ${cse(t.performance)}ms FPS process:${l} refresh:${u}<br>
-    ${c}<br>
-  `,he.framesDraw++,he.lastFrame=performance.now(),he.buffered?he.drawThread=requestAnimationFrame(()=>t1(e,n)):!he.buffered&&he.drawThread&&($n("stopping buffered refresh"),cancelAnimationFrame(he.drawThread),he.drawThread=null)}async function n1(){var u;if(he.busy)return null;he.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(tr("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=`
-${a}`,$n(a),tr(a),he.busy=!1,a;let s,i={audio:!1,video:{facingMode:he.facing?"user":"environment",resizeMode:he.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(c){return c.name==="PermissionDeniedError"||c.name==="NotAllowedError"?a="camera permission denied":c.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${c.message||c}`,n.innerText+=`
-${a}`,tr(a),$n("camera error:",c),he.busy=!1,a}if(s)e.srcObject=s;else return he.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return he.camera={name:(u=o.label)==null?void 0:u.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(c=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",he.menuWidth.input.setAttribute("value",e.width),he.menuHeight.input.setAttribute("value",e.height),r&&e.play(),r&&!he.detectThread&&sh(e,t),he.busy=!1,tr(""),c()}})}function P4(){if(!Ui){let e=null;Ui=new O4(e,{trackGPU:!1,chartHz:20,chartLen:20}),Ui.begin()}}function hse(e,t,n,r){Q0||($n("creating worker thread"),Q0=new Worker(he.worker,{type:"module"}),Q0.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&he.detectFPS.push(1e3/a.data.result.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Ui||P4(),Ui.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),e1=a.data.result,he.framesDetect++,he.drawThread||t1(e),he.detectThread=requestAnimationFrame(s=>sh(e,n,s))})),Q0.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:os},[t.data.buffer])}function sh(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){he.drawThread&&cancelAnimationFrame(he.drawThread),he.detectThread&&cancelAnimationFrame(he.detectThread),he.drawThread=null,he.detectThread=null,e.paused?$n("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>sh(e,t),500):$n(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(he.drawThread),he.drawThread=null,$n("frame statistics: process:",he.framesDetect,"refresh:",he.framesDraw),$n("memory",re.tf.engine().memory());return}if(tr(""),he.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);hse(e,o,t,os,n)}else re.detect(e,os).then(s=>{s.performance&&s.performance.total&&he.detectFPS.push(1e3/s.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Ui||P4(),Ui.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),s.error?($n(s.error),document.getElementById("log").innerText+=`
-Human error: ${s.error}`):(e1=s,he.drawThread||t1(e),he.framesDetect++,he.detectThread=requestAnimationFrame(i=>sh(e,t,i)))})}async function dse(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{$n("processing image:",encodeURI(n.src));let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=re.config.filter.width&&re.config.filter.width>0?re.config.filter.width:n.naturalWidth,r.height=re.config.filter.height&&re.config.filter.height>0?re.config.filter.height:n.naturalHeight;let a=await re.detect(n,os);e1=a,await t1(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(he.columns+.1),s.height=s.width*r.height/r.width,a.face&&a.face.length>0?s.title=a.face.map((o,l)=>`#${l} face: ${Math.trunc(100*o.faceConfidence)}% box: ${Math.trunc(100*o.boxConfidence)}% age: ${Math.trunc(o.age)} gender: ${Math.trunc(100*o.genderConfidence)}% ${o.gender}`).join(" | "):s.title="no face detected",s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function L4(){document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start<br>video",tr("paused"),e.pause();else{let n=await n1();if(n)tr(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(Ae))r.hide();tr(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause<br>video",await e.play(),he.detectThread||sh(e,t)}}}async function pse(){os.videoOptimized=!1,document.getElementById("play").style.display="none",document.getElementById("canvas").style.display="none",document.getElementById("samples-container").style.display="block",$n("running detection of sample images"),tr("processing images"),document.getElementById("samples-container").innerHTML="";for(let e of Object.values(Ae))e.hide();for(let e of he.samples)await dse(e);tr("")}function fse(){let e=[];window.innerWidth>800?e=[`${document.getElementById("btnDisplay").offsetLeft-50}px`,`${document.getElementById("btnImage").offsetLeft-50}px`,`${document.getElementById("btnProcess").offsetLeft-50}px`,`${document.getElementById("btnModel").offsetLeft-50}px`]:e=["0rem","11rem","21.1rem","33rem"],Ae.display=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[0]}),Ae.display.addBool("perf monitor",he,"bench",t=>he.bench=t),Ae.display.addBool("buffered output",he,"buffered",t=>he.buffered=t),Ae.display.addBool("crop & scale",he,"crop",t=>{he.crop=t,n1()}),Ae.display.addBool("camera facing",he,"facing",t=>{he.facing=t,n1()}),Ae.display.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.display.addBool("use 3D depth",re.draw.drawOptions,"useDepth"),Ae.display.addBool("draw with curves",re.draw.drawOptions,"useCurves"),Ae.display.addBool("print labels",re.draw.drawOptions,"drawLabels"),Ae.display.addBool("draw points",re.draw.drawOptions,"drawPoints"),Ae.display.addBool("draw boxes",re.draw.drawOptions,"drawBoxes"),Ae.display.addBool("draw polygons",re.draw.drawOptions,"drawPolygons"),Ae.display.addBool("fill polygons",re.draw.drawOptions,"fillPolygons"),Ae.image=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),Ae.image.addBool("enabled",re.config.filter,"enabled",t=>re.config.filter.enabled=t),he.menuWidth=Ae.image.addRange("image width",re.config.filter,"width",0,3840,10,t=>re.config.filter.width=parseInt(t)),he.menuHeight=Ae.image.addRange("image height",re.config.filter,"height",0,2160,10,t=>re.config.filter.height=parseInt(t)),Ae.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.image.addRange("brightness",re.config.filter,"brightness",-1,1,.05,t=>re.config.filter.brightness=parseFloat(t)),Ae.image.addRange("contrast",re.config.filter,"contrast",-1,1,.05,t=>re.config.filter.contrast=parseFloat(t)),Ae.image.addRange("sharpness",re.config.filter,"sharpness",0,1,.05,t=>re.config.filter.sharpness=parseFloat(t)),Ae.image.addRange("blur",re.config.filter,"blur",0,20,1,t=>re.config.filter.blur=parseInt(t)),Ae.image.addRange("saturation",re.config.filter,"saturation",-1,1,.05,t=>re.config.filter.saturation=parseFloat(t)),Ae.image.addRange("hue",re.config.filter,"hue",0,360,5,t=>re.config.filter.hue=parseInt(t)),Ae.image.addRange("pixelate",re.config.filter,"pixelate",0,32,1,t=>re.config.filter.pixelate=parseInt(t)),Ae.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.image.addBool("negative",re.config.filter,"negative",t=>re.config.filter.negative=t),Ae.image.addBool("sepia",re.config.filter,"sepia",t=>re.config.filter.sepia=t),Ae.image.addBool("vintage",re.config.filter,"vintage",t=>re.config.filter.vintage=t),Ae.image.addBool("kodachrome",re.config.filter,"kodachrome",t=>re.config.filter.kodachrome=t),Ae.image.addBool("technicolor",re.config.filter,"technicolor",t=>re.config.filter.technicolor=t),Ae.image.addBool("polaroid",re.config.filter,"polaroid",t=>re.config.filter.polaroid=t),Ae.process=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[2]}),Ae.process.addList("backend",["cpu","webgl","wasm","humangl"],re.config.backend,t=>re.config.backend=t),Ae.process.addBool("async operations",re.config,"async",t=>re.config.async=t),Ae.process.addBool("use web worker",he,"useWorker"),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addLabel("model parameters"),Ae.process.addRange("max objects",re.config.face.detector,"maxFaces",1,50,1,t=>{re.config.face.detector.maxFaces=parseInt(t),re.config.body.maxDetections=parseInt(t),re.config.hand.maxHands=parseInt(t)}),Ae.process.addRange("skip frames",re.config.face.detector,"skipFrames",0,50,1,t=>{re.config.face.detector.skipFrames=parseInt(t),re.config.face.emotion.skipFrames=parseInt(t),re.config.face.age.skipFrames=parseInt(t),re.config.hand.skipFrames=parseInt(t)}),Ae.process.addRange("min confidence",re.config.face.detector,"minConfidence",0,1,.05,t=>{re.config.face.detector.minConfidence=parseFloat(t),re.config.face.gender.minConfidence=parseFloat(t),re.config.face.emotion.minConfidence=parseFloat(t),re.config.hand.minConfidence=parseFloat(t)}),Ae.process.addRange("score threshold",re.config.face.detector,"scoreThreshold",.1,1,.05,t=>{re.config.face.detector.scoreThreshold=parseFloat(t),re.config.hand.scoreThreshold=parseFloat(t),re.config.body.scoreThreshold=parseFloat(t)}),Ae.process.addRange("overlap",re.config.face.detector,"iouThreshold",.1,1,.05,t=>{re.config.face.detector.iouThreshold=parseFloat(t),re.config.hand.iouThreshold=parseFloat(t)}),Ae.process.addBool("detection rotation",re.config.face.detector,"rotation",t=>{re.config.face.detector.rotation=t,re.config.hand.rotation=t}),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addButton("process sample images","process images",()=>pse()),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addChart("FPS","FPS"),Ae.models=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),Ae.models.addBool("face detect",re.config.face,"enabled",t=>re.config.face.enabled=t),Ae.models.addBool("face mesh",re.config.face.mesh,"enabled",t=>re.config.face.mesh.enabled=t),Ae.models.addBool("face iris",re.config.face.iris,"enabled",t=>re.config.face.iris.enabled=t),Ae.models.addBool("face description",re.config.face.description,"enabled",t=>re.config.face.age.description=t),Ae.models.addBool("face emotion",re.config.face.emotion,"enabled",t=>re.config.face.emotion.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("body pose",re.config.body,"enabled",t=>re.config.body.enabled=t),Ae.models.addBool("hand pose",re.config.hand,"enabled",t=>re.config.hand.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("gestures",re.config.gesture,"enabled",t=>re.config.gesture.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("object detection",re.config.object,"enabled",t=>re.config.object.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("face compare",wa,"enabled",t=>{wa.enabled=t,wa.original=null}),document.getElementById("btnDisplay").addEventListener("click",t=>Ae.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>Ae.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>Ae.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>Ae.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>L4()),document.getElementById("play").addEventListener("click",()=>L4())}async function mse(e){let t=document.getElementById("canvas");t.width=e.canvas.width,t.height=e.canvas.height,t.getContext("2d").drawImage(e.canvas,0,0,e.canvas.width,e.canvas.height,0,0,t.width,t.height),await re.draw.all(t,e)}async function Ase(){if($n("demo starting ..."),fse(),document.getElementById("log").innerText=`Human: version ${re.version}`,he.modelsPreload&&!he.useWorker){tr("loading"),await re.load(os);let e=Object.keys(re.models).filter(t=>re.models[t]);$n("demo loaded models:",e)}if(!he.useWorker){tr("initializing");let e=await re.warmup(os);e&&e.canvas&&he.drawWarmup&&await mse(e)}tr("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",$n("demo ready...")}window.onload=Ase;window.onresize=n1;
+    performance: ${lse(t.performance)}ms FPS process:${l} refresh:${c}<br>
+    ${u}<br>
+  `,he.framesDraw++,he.lastFrame=performance.now(),he.buffered?he.drawThread=requestAnimationFrame(()=>r1(e,n)):!he.buffered&&he.drawThread&&($n("stopping buffered refresh"),cancelAnimationFrame(he.drawThread),he.drawThread=null)}async function a1(){var c;if(he.busy)return null;he.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(tr("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=`
+${a}`,$n(a),tr(a),he.busy=!1,a;let s,i={audio:!1,video:{facingMode:he.facing?"user":"environment",resizeMode:he.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(u){return u.name==="PermissionDeniedError"||u.name==="NotAllowedError"?a="camera permission denied":u.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${u.message||u}`,n.innerText+=`
+${a}`,tr(a),$n("camera error:",u),he.busy=!1,a}if(s)e.srcObject=s;else return he.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return he.camera={name:(c=o.label)==null?void 0:c.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(u=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",he.menuWidth.input.setAttribute("value",e.width),he.menuHeight.input.setAttribute("value",e.height),r&&e.play(),r&&!he.detectThread&&sh(e,t),he.busy=!1,tr(""),u()}})}function z4(){if(!Ui){let e=null;Ui=new D4(e,{trackGPU:!1,chartHz:20,chartLen:20}),Ui.begin()}}function use(e,t,n,r){t1||($n("creating worker thread"),t1=new Worker(he.worker,{type:"module"}),t1.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&he.detectFPS.push(1e3/a.data.result.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Ui||z4(),Ui.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),n1=a.data.result,he.framesDetect++,he.drawThread||r1(e),he.detectThread=requestAnimationFrame(s=>sh(e,n,s))})),t1.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:os},[t.data.buffer])}function sh(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){he.drawThread&&cancelAnimationFrame(he.drawThread),he.detectThread&&cancelAnimationFrame(he.detectThread),he.drawThread=null,he.detectThread=null,e.paused?$n("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>sh(e,t),500):$n(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(he.drawThread),he.drawThread=null,$n("frame statistics: process:",he.framesDetect,"refresh:",he.framesDraw),$n("memory",re.tf.engine().memory());return}if(tr(""),he.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);use(e,o,t,os,n)}else re.detect(e,os).then(s=>{s.performance&&s.performance.total&&he.detectFPS.push(1e3/s.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Ui||z4(),Ui.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),s.error?($n(s.error),document.getElementById("log").innerText+=`
+Human error: ${s.error}`):(n1=s,he.drawThread||r1(e),he.framesDetect++,he.detectThread=requestAnimationFrame(i=>sh(e,t,i)))})}async function hse(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{$n("processing image:",encodeURI(n.src));let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=re.config.filter.width&&re.config.filter.width>0?re.config.filter.width:n.naturalWidth,r.height=re.config.filter.height&&re.config.filter.height>0?re.config.filter.height:n.naturalHeight;let a=await re.detect(n,os);n1=a,await r1(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(he.columns+.1),s.height=s.width*r.height/r.width,a.face&&a.face.length>0?s.title=a.face.map((o,l)=>`#${l} face: ${Math.trunc(100*o.faceConfidence)}% box: ${Math.trunc(100*o.boxConfidence)}% age: ${Math.trunc(o.age)} gender: ${Math.trunc(100*o.genderConfidence)}% ${o.gender}`).join(" | "):s.title="no face detected",s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function P4(){document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start<br>video",tr("paused"),e.pause();else{let n=await a1();if(n)tr(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(Ae))r.hide();tr(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause<br>video",await e.play(),he.detectThread||sh(e,t)}}}async function dse(){os.videoOptimized=!1,document.getElementById("play").style.display="none",document.getElementById("canvas").style.display="none",document.getElementById("samples-container").style.display="block",$n("running detection of sample images"),tr("processing images"),document.getElementById("samples-container").innerHTML="";for(let e of Object.values(Ae))e.hide();for(let e of he.samples)await hse(e);tr("")}function pse(){let e=[];window.innerWidth>800?e=[`${document.getElementById("btnDisplay").offsetLeft-50}px`,`${document.getElementById("btnImage").offsetLeft-50}px`,`${document.getElementById("btnProcess").offsetLeft-50}px`,`${document.getElementById("btnModel").offsetLeft-50}px`]:e=["0rem","11rem","21.1rem","33rem"],Ae.display=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[0]}),Ae.display.addBool("perf monitor",he,"bench",t=>he.bench=t),Ae.display.addBool("buffered output",he,"buffered",t=>he.buffered=t),Ae.display.addBool("crop & scale",he,"crop",t=>{he.crop=t,a1()}),Ae.display.addBool("camera facing",he,"facing",t=>{he.facing=t,a1()}),Ae.display.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.display.addBool("use 3D depth",re.draw.drawOptions,"useDepth"),Ae.display.addBool("draw with curves",re.draw.drawOptions,"useCurves"),Ae.display.addBool("print labels",re.draw.drawOptions,"drawLabels"),Ae.display.addBool("draw points",re.draw.drawOptions,"drawPoints"),Ae.display.addBool("draw boxes",re.draw.drawOptions,"drawBoxes"),Ae.display.addBool("draw polygons",re.draw.drawOptions,"drawPolygons"),Ae.display.addBool("fill polygons",re.draw.drawOptions,"fillPolygons"),Ae.image=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),Ae.image.addBool("enabled",re.config.filter,"enabled",t=>re.config.filter.enabled=t),he.menuWidth=Ae.image.addRange("image width",re.config.filter,"width",0,3840,10,t=>re.config.filter.width=parseInt(t)),he.menuHeight=Ae.image.addRange("image height",re.config.filter,"height",0,2160,10,t=>re.config.filter.height=parseInt(t)),Ae.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.image.addRange("brightness",re.config.filter,"brightness",-1,1,.05,t=>re.config.filter.brightness=parseFloat(t)),Ae.image.addRange("contrast",re.config.filter,"contrast",-1,1,.05,t=>re.config.filter.contrast=parseFloat(t)),Ae.image.addRange("sharpness",re.config.filter,"sharpness",0,1,.05,t=>re.config.filter.sharpness=parseFloat(t)),Ae.image.addRange("blur",re.config.filter,"blur",0,20,1,t=>re.config.filter.blur=parseInt(t)),Ae.image.addRange("saturation",re.config.filter,"saturation",-1,1,.05,t=>re.config.filter.saturation=parseFloat(t)),Ae.image.addRange("hue",re.config.filter,"hue",0,360,5,t=>re.config.filter.hue=parseInt(t)),Ae.image.addRange("pixelate",re.config.filter,"pixelate",0,32,1,t=>re.config.filter.pixelate=parseInt(t)),Ae.image.addHTML('<hr style="border-style: inset; 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border-color: dimgray">'),Ae.process.addButton("process sample images","process images",()=>dse()),Ae.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.process.addChart("FPS","FPS"),Ae.models=new ah(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),Ae.models.addBool("face detect",re.config.face,"enabled",t=>re.config.face.enabled=t),Ae.models.addBool("face mesh",re.config.face.mesh,"enabled",t=>re.config.face.mesh.enabled=t),Ae.models.addBool("face iris",re.config.face.iris,"enabled",t=>re.config.face.iris.enabled=t),Ae.models.addBool("face description",re.config.face.description,"enabled",t=>re.config.face.age.description=t),Ae.models.addBool("face emotion",re.config.face.emotion,"enabled",t=>re.config.face.emotion.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("body pose",re.config.body,"enabled",t=>re.config.body.enabled=t),Ae.models.addBool("hand pose",re.config.hand,"enabled",t=>re.config.hand.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("gestures",re.config.gesture,"enabled",t=>re.config.gesture.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("object detection",re.config.object,"enabled",t=>re.config.object.enabled=t),Ae.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),Ae.models.addBool("face compare",wa,"enabled",t=>{wa.enabled=t,wa.original=null}),document.getElementById("btnDisplay").addEventListener("click",t=>Ae.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>Ae.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>Ae.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>Ae.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>P4()),document.getElementById("play").addEventListener("click",()=>P4())}async function fse(e){let t=document.getElementById("canvas");t.width=e.canvas.width,t.height=e.canvas.height,t.getContext("2d").drawImage(e.canvas,0,0,e.canvas.width,e.canvas.height,0,0,t.width,t.height),await re.draw.all(t,e)}async function mse(){if($n("demo starting ..."),pse(),document.getElementById("log").innerText=`Human: version ${re.version}`,he.modelsPreload&&!he.useWorker){tr("loading"),await re.load(os);let e=Object.keys(re.models).filter(t=>re.models[t]);$n("demo loaded models:",e)}if(!he.useWorker){tr("initializing");let e=await re.warmup(os);e&&e.canvas&&he.drawWarmup&&await fse(e)}tr("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",$n("demo ready...")}window.onload=mse;window.onresize=a1;
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/demo-browser-index.js.map b/dist/demo-browser-index.js.map
index aabc9b97..fa42291d 100644
--- a/dist/demo-browser-index.js.map
+++ b/dist/demo-browser-index.js.map
@@ -1,7 +1,7 @@
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"../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/imagefx.js", "../src/image/image.ts", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts", "../src/human.ts", "../demo/menu.js", "../demo/gl-bench.js", "../demo/browser.js"],
-  "sourcesContent": ["// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var 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msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var 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_sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 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asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=function(){return(___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=Module[\"asm\"][\"db\"]).apply(null,arguments)};var _emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=function(){return(_emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=Module[\"asm\"][\"eb\"]).apply(null,arguments)};var _emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=function(){return(_emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=Module[\"asm\"][\"fb\"]).apply(null,arguments)};var _emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n  return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var 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doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(name: string, raw: any): void {\n  if (!raw || !raw.kernels) return;\n  const maxResults = 5;\n  const time = raw.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n  data[name] = res;\n  log('Human profiler', name, res);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    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y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\nconst activateScore = false;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position\n          if (score > config.object.minConfidence) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            let boxRaw = [ // results normalized to range 0..1\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n              cx + (scaleBox / strideSize * boxOffset[2]),\n              cy + (scaleBox / strideSize * boxOffset[3]),\n            ];\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n  const nmsIdx = nms.dataSync();\n  tf.dispose(nms);\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    // @ts-ignore\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    resize.dispose();\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result.clone();\n      profileObject.result.dispose();\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      tf.enableDebugMode();\n      */\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "let instance = 0;\nlet CSScreated = false;\n\nlet theme = {\n  background: 'darkslategray',\n  hover: 'lightgray',\n  itemBackground: 'black',\n  itemColor: 'white',\n  buttonBackground: 'lightblue',\n  buttonHover: 'lightgreen',\n  checkboxOn: 'lightgreen',\n  checkboxOff: 'lightcoral',\n  rangeBackground: 'lightblue',\n  rangeLabel: 'white',\n  chartColor: 'lightblue',\n};\n\nfunction createCSS() {\n  if (CSScreated) return;\n  const css = `\n  :root { --rounded: 0.1rem; }\n  .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;\n          box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }\n\n  .menu:hover { box-shadow: 0 0 8px ${theme.hover}; }\n  .menu-container { display: block; max-height: 100vh; }\n  .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }\n  .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }\n  .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }\n  .menu-title { cursor: pointer; }\n  .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }\n  .menu-label { padding: 0; font-weight: 800; }\n\n  .menu-list { margin-right: 0.8rem; }\n  select:focus { outline: none; }\n  .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;\n    font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }\n\n  .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}\n  .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }\n  \n  .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;\n    border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }\n  .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }\n  .menu-button:focus { outline: none; }\n\n  .menu-checkbox { width: 2.8rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }\n  .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n  .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n  .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${theme.checkboxOff};\n    border-radius: var(--rounded); transition: left 0.6s ease; }\n\n  input[type=checkbox] { visibility: hidden; }\n  input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }\n\n  .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${theme.rangeBackground}; }\n  .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }\n\n  input[type=range] { -webkit-appearance: none; }\n  input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n  input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n  input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n  input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n\n  .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }\n  .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }\n  `;\n  const el = document.createElement('style');\n  el.innerHTML = css;\n  document.getElementsByTagName('head')[0].appendChild(el);\n  CSScreated = true;\n}\n\nclass Menu {\n  constructor(parent, title, position, userTheme) {\n    if (userTheme) theme = { ...theme, ...userTheme };\n    createCSS();\n    this.createMenu(parent, title, position);\n    this.id = 0;\n    this.instance = instance;\n    instance++;\n    this._maxFPS = 0;\n    this.hidden = 0;\n  }\n\n  createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {\n    this.menu = document.createElement('div');\n    this.menu.id = `menu-${instance}`;\n    this.menu.className = 'menu';\n    if (position) {\n      if (position.top) this.menu.style.top = position.top;\n      if (position.bottom) this.menu.style.bottom = position.bottom;\n      if (position.left) this.menu.style.left = position.left;\n      if (position.right) this.menu.style.right = position.right;\n    }\n\n    this.container = document.createElement('div');\n    this.container.id = `menu-container-${instance}`;\n    this.container.className = 'menu-container menu-container-fadein';\n\n    // set menu title with pulldown arrow\n    const elTitle = document.createElement('div');\n    elTitle.className = 'menu-title';\n    elTitle.id = `menu-title-${instance}`;\n    const svg = `<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 448 512\" style=\"width: 2rem; height: 2rem; vertical-align: top;\">\n        <path d=\"M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h352a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48zm-51.37 182.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z\" class=\"svg-background\"/>\n        <path d=\"M348.63 214.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z\" class=\"svg-foreground\"/>\n      </svg>`;\n    if (title) elTitle.innerHTML = `${title}${svg}`;\n    this.menu.appendChild(elTitle);\n    elTitle.addEventListener('click', () => {\n      this.container.classList.toggle('menu-container-fadeout');\n      this.container.classList.toggle('menu-container-fadein');\n      this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';\n    });\n\n    this.menu.appendChild(this.container);\n    if (typeof parent === 'object') parent.appendChild(this.menu);\n    else document.getElementById(parent).appendChild(this.menu);\n  }\n\n  get newID() {\n    this.id++;\n    return `menu-${this.instance}-${this.id}`;\n  }\n\n  get ID() {\n    return `menu-${this.instance}-${this.id}`;\n  }\n\n  get width() {\n    return this.menu.offsetWidth;\n  }\n\n  get height() {\n    return this.menu.offsetHeight;\n  }\n\n  hide() {\n    if (this.container.classList.contains('menu-container-fadein')) {\n      this.container.classList.toggle('menu-container-fadeout');\n      this.container.classList.toggle('menu-container-fadein');\n    }\n  }\n\n  visible() {\n    return (this.container.classList.contains('menu-container-fadein'));\n  }\n\n  toggle(evt) {\n    this.container.classList.toggle('menu-container-fadeout');\n    this.container.classList.toggle('menu-container-fadein');\n    if (this.container.classList.contains('menu-container-fadein') && evt) {\n      const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);\n      // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);\n      if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;\n      // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;\n      if (this.menu.offsetLeft < 0) this.menu.style.left = 0;\n      if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {\n        this.menu.style.left = null;\n        this.menu.style.right = 0;\n      }\n      this.menu.style.borderStyle = 'solid';\n    } else {\n      this.menu.style.borderStyle = 'none';\n    }\n  }\n\n  addTitle(title) {\n    const el = document.createElement('div');\n    el.className = 'menu-title';\n    el.id = this.newID;\n    el.innerHTML = title;\n    this.menu.appendChild(el);\n    el.addEventListener('click', () => {\n      this.hidden = !this.hidden;\n      const all = document.getElementsByClassName('menu');\n      for (const item of all) {\n        item.style.display = this.hidden ? 'none' : 'block';\n      }\n    });\n    return el;\n  }\n\n  addLabel(title) {\n    const el = document.createElement('div');\n    el.className = 'menu-item menu-label';\n    el.id = this.newID;\n    el.innerHTML = title;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  addBool(title, object, variable, callback) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.innerHTML = `<div class=\"menu-checkbox\"><input class=\"menu-checkbox\" type=\"checkbox\" id=\"${this.newID}\" ${object[variable] ? 'checked' : ''}/><label class=\"menu-checkbox-label\" for=\"${this.ID}\"></label></div>${title}`;\n    this.container.appendChild(el);\n    el.addEventListener('change', (evt) => {\n      object[variable] = evt.target.checked;\n      if (callback) callback(evt.target.checked);\n    });\n    return el;\n  }\n\n  async addList(title, items, selected, callback) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    let options = '';\n    for (const item of items) {\n      const def = item === selected ? 'selected' : '';\n      options += `<option value=\"${item}\" ${def}>${item}</option>`;\n    }\n    el.innerHTML = `<div class=\"menu-list\"><select name=\"${this.ID}\" class=\"menu-list-item\">${options}</select><label for=\"${this.ID}\"></label></div>${title}`;\n    el.style.fontFamily = document.body.style.fontFamily;\n    el.style.fontSize = document.body.style.fontSize;\n    el.style.fontVariant = document.body.style.fontVariant;\n    this.container.appendChild(el);\n    el.addEventListener('change', (evt) => {\n      if (callback) callback(items[evt.target.selectedIndex]);\n    });\n    return el;\n  }\n\n  addRange(title, object, variable, min, max, step, callback) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.innerHTML = `<input class=\"menu-range\" type=\"range\" id=\"${this.newID}\" min=\"${min}\" max=\"${max}\" step=\"${step}\" value=\"${object[variable]}\">${title}`;\n    this.container.appendChild(el);\n    el.addEventListener('change', (evt) => {\n      object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);\n      evt.target.setAttribute('value', evt.target.value);\n      if (callback) callback(evt.target.value);\n    });\n    el.input = el.children[0];\n    return el;\n  }\n\n  addHTML(html) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.id = this.newID;\n    if (html) el.innerHTML = html;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  addButton(titleOn, titleOff, callback) {\n    const el = document.createElement('button');\n    el.className = 'menu-item menu-button';\n    el.style.fontFamily = document.body.style.fontFamily;\n    el.style.fontSize = document.body.style.fontSize;\n    el.style.fontVariant = document.body.style.fontVariant;\n    el.type = 'button';\n    el.id = this.newID;\n    el.innerText = titleOn;\n    this.container.appendChild(el);\n    el.addEventListener('click', () => {\n      if (el.innerText === titleOn) el.innerText = titleOff;\n      else el.innerText = titleOn;\n      if (callback) callback(el.innerText !== titleOn);\n    });\n    return el;\n  }\n\n  addValue(title, val, suffix = '') {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.id = `menu-val-${title}`;\n    el.innerText = `${title}: ${val}${suffix}`;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  updateValue(title, val, suffix = '') {\n    const el = document.getElementById(`menu-val-${title}`);\n    if (el) el.innerText = `${title}: ${val}${suffix}`;\n    else this.addValue(title, val);\n  }\n\n  addChart(title, id, width = 150, height = 40, color) {\n    if (color) theme.chartColor = color;\n    const el = document.createElement('div');\n    el.className = 'menu-item menu-chart-title';\n    el.id = this.newID;\n    el.innerHTML = `<font color=${theme.chartColor}>${title}</font><canvas id=\"menu-canvas-${id}\" class=\"menu-chart-canvas\" width=\"${width}px\" height=\"${height}px\"></canvas>`;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  async updateChart(id, values) {\n    if (!values || (values.length === 0)) return;\n    const canvas = document.getElementById(`menu-canvas-${id}`);\n    if (!canvas) return;\n    const ctx = canvas.getContext('2d');\n    ctx.fillStyle = theme.background;\n    ctx.fillRect(0, 0, canvas.width, canvas.height);\n    const width = canvas.width / values.length;\n    const max = 1 + Math.max(...values);\n    const height = canvas.height / max;\n    for (let i = 0; i < values.length; i++) {\n      const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);\n      gradient.addColorStop(0.1, theme.chartColor);\n      gradient.addColorStop(0.4, theme.background);\n      ctx.fillStyle = gradient;\n      ctx.fillRect(i * width, 0, width - 4, canvas.height);\n      ctx.fillStyle = theme.background;\n      ctx.font = `${width / 1.5}px \"Segoe UI\"`;\n      ctx.fillText(Math.round(values[i]), i * width + 1, canvas.height - 1, width - 1);\n    }\n  }\n}\n\nexport default Menu;\n", "/* eslint-disable max-len */\n\n// based on: https://github.com/munrocket/gl-bench\n\nconst UICSS = `\n  #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }\n  #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 2px 0 2px; background: darkslategray; border-radius: 0.1rem; cursor: pointer; opacity: 0.9; }\n  #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }\n  #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }\n  #gl-bench .gl-mem { font-size: 12px; fill: white; }\n  #gl-bench .gl-fps { font-size: 13px; fill: white; }\n  #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }\n  #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }\n  #gl-bench rect { fill: black; }\n  #gl-bench .opacity { stroke: black; }\n  `;\n\nconst UISVG = `\n  <div class=\"gl-box\">\n    <svg viewBox=\"0 0 60 60\">\n      <text x=\"27\" y=\"56\" class=\"gl-fps\">00 FPS</text>\n      <text x=\"30\" y=\"8\" class=\"gl-mem\"></text>\n      <rect x=\"0\" y=\"14\" rx=\"4\" ry=\"4\" width=\"60\" height=\"32\"></rect>\n      <polyline class=\"gl-chart\"></polyline>\n    </svg>\n    <svg viewBox=\"0 0 14 60\" class=\"gl-cpu-svg\">\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"opacity\"/>\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"gl-cpu\" stroke-dasharray=\"0 27\"/>\n      <path d=\"M5.35 43c-.464 0-.812.377-.812.812v1.16c-.783.1972-1.421.812-1.595 1.624h-1.16c-.435 0-.812.348-.812.812s.348.812.812.812h1.102v1.653H1.812c-.464 0-.812.377-.812.812 0 .464.377.812.812.812h1.131c.1943.783.812 1.392 1.595 1.595v1.131c0 .464.377.812.812.812.464 0 .812-.377.812-.812V53.15h1.653v1.073c0 .464.377.812.812.812.464 0 .812-.377.812-.812v-1.131c.783-.1943 1.392-.812 1.595-1.595h1.131c.464 0 .812-.377.812-.812 0-.464-.377-.812-.812-.812h-1.073V48.22h1.102c.435 0 .812-.348.812-.812s-.348-.812-.812-.812h-1.16c-.1885-.783-.812-1.421-1.595-1.624v-1.131c0-.464-.377-.812-.812-.812-.464 0-.812.377-.812.812v1.073H6.162v-1.073c0-.464-.377-.812-.812-.812zm.58 3.48h2.088c.754 0 1.363.609 1.363 1.363v2.088c0 .754-.609 1.363-1.363 1.363H5.93c-.754 0-1.363-.609-1.363-1.363v-2.088c0-.754.609-1.363 1.363-1.363z\" style=\"fill: grey\"></path>\n    </svg>\n    <svg viewBox=\"0 0 14 60\" class=\"gl-gpu-svg\">\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"opacity\"/>\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"gl-gpu\" stroke-dasharray=\"0 27\"/>\n      <path d=\"M1.94775 43.3772a.736.736 0 10-.00416 1.472c.58535.00231.56465.1288.6348.3197.07015.18975.04933.43585.04933.43585l-.00653.05405v8.671a.736.736 0 101.472 0v-1.4145c.253.09522.52785.1495.81765.1495h5.267c1.2535 0 2.254-.9752 2.254-2.185v-3.105c0-1.2075-1.00625-2.185-2.254-2.185h-5.267c-.28865 0-.5635.05405-.8165.1495.01806-.16445.04209-.598-.1357-1.0787-.22425-.6072-.9499-1.2765-2.0125-1.2765zm2.9095 3.6455c.42435 0 .7659.36225.7659.8119v2.9785c0 .44965-.34155.8119-.7659.8119s-.7659-.36225-.7659-.8119v-2.9785c0-.44965.34155-.8119.7659-.8119zm4.117 0a2.3 2.3 0 012.3 2.3 2.3 2.3 0 01-2.3 2.3 2.3 2.3 0 01-2.3-2.3 2.3 2.3 0 012.3-2.3z\" style=\"fill: grey\"></path>\n    </svg>\n  </div>\n  `;\n\nclass GLBench {\n  /** GLBench constructor\n   * @param { WebGLRenderingContext | WebGL2RenderingContext } gl context\n   * @param { Object | undefined } settings additional settings\n   */\n  constructor(gl, settings = {}) {\n    this.css = UICSS;\n    this.svg = UISVG;\n    // eslint-disable-next-line @typescript-eslint/no-empty-function\n    this.paramLogger = () => {};\n    // eslint-disable-next-line @typescript-eslint/no-empty-function\n    this.chartLogger = () => {};\n    this.chartLen = 20;\n    this.chartHz = 20;\n\n    this.names = [];\n    this.cpuAccums = [];\n    this.gpuAccums = [];\n    this.activeAccums = [];\n    this.chart = new Array(this.chartLen);\n    this.now = () => ((performance && performance.now) ? performance.now() : Date.now());\n    this.updateUI = () => {\n      [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');\n    };\n\n    Object.assign(this, settings);\n    this.detected = 0;\n    this.finished = [];\n    this.isFramebuffer = 0;\n    this.frameId = 0;\n\n    // 120hz device detection\n    let rafId; let n = 0; let\n      t0;\n    const loop = (t) => {\n      if (++n < 20) {\n        rafId = requestAnimationFrame(loop);\n      } else {\n        this.detected = Math.ceil(1e3 * n / (t - t0) / 70);\n        cancelAnimationFrame(rafId);\n      }\n      if (!t0) t0 = t;\n    };\n    requestAnimationFrame(loop);\n\n    // attach gpu profilers\n    if (gl) {\n      const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {\n        gl.getError();\n        const dt = this.now() - t;\n        activeAccums.forEach((active, i) => {\n          if (active) this.gpuAccums[i] += dt;\n        });\n      }, 0));\n\n      const addProfiler = (fn, self, target) => {\n        const t = self.now();\n        // eslint-disable-next-line prefer-rest-params\n        fn.apply(target, arguments);\n        if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));\n      };\n\n      /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {\n        if (gl[fn]) {\n          gl[fn] = addProfiler(gl[fn], this, gl);\n        }\n      });\n      */\n      const fn = 'drawElements';\n      if (gl[fn]) {\n        gl[fn] = addProfiler(gl[fn], this, gl);\n      } else {\n        // eslint-disable-next-line no-console\n        console.log('bench: cannot attach to webgl function');\n      }\n\n      /*\n      gl.getExtension = ((fn, self) => {\n        // eslint-disable-next-line prefer-rest-params\n        const ext = fn.apply(gl, arguments);\n        if (ext) {\n          ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {\n            if (ext[fn2]) {\n              ext[fn2] = addProfiler(ext[fn2], self, ext);\n            }\n          });\n        }\n        return ext;\n      })(gl.getExtension, this);\n      */\n    }\n\n    // init ui and ui loggers\n    if (!this.withoutUI) {\n      if (!this.dom) this.dom = document.body;\n      const elm = document.createElement('div');\n      elm.id = 'gl-bench';\n      this.dom.appendChild(elm);\n      this.dom.insertAdjacentHTML('afterbegin', '<style id=\"gl-bench-style\">' + this.css + '</style>');\n      this.dom = elm;\n      this.dom.addEventListener('click', () => {\n        this.trackGPU = !this.trackGPU;\n        this.updateUI();\n      });\n\n      this.paramLogger = ((logger, dom, names) => {\n        const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];\n        const nodes = { ...classes };\n        classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));\n        this.nodes = nodes;\n        return (i, cpu, gpu, mem, fps, totalTime, frameId) => {\n          nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';\n          nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';\n          // eslint-disable-next-line no-nested-ternary\n          nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');\n          nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);\n          logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);\n        };\n      })(this.paramLogger, this.dom, this.names);\n\n      this.chartLogger = ((logger, dom) => {\n        const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };\n        return (i, chart, circularId) => {\n          let points = '';\n          const len = chart.length;\n          for (let j = 0; j < len; j++) {\n            const id = (circularId + j + 1) % len;\n            if (chart[id] !== undefined) points = points + ' ' + (60 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 0.5 / this.detected).toFixed(1);\n          }\n          nodes['gl-chart'][i].setAttribute('points', points);\n          logger(this.names[i], chart, circularId);\n        };\n      })(this.chartLogger, this.dom);\n    }\n  }\n\n  /**\n   * Explicit UI add\n   * @param { string | undefined } name\n   */\n  addUI(name) {\n    if (this.names.indexOf(name) === -1) {\n      this.names.push(name);\n      if (this.dom) {\n        this.dom.insertAdjacentHTML('beforeend', this.svg);\n        this.updateUI();\n      }\n      this.cpuAccums.push(0);\n      this.gpuAccums.push(0);\n      this.activeAccums.push(false);\n    }\n  }\n\n  /**\n   * Increase frameID\n   * @param { number | undefined } now\n   */\n  nextFrame(now) {\n    this.frameId++;\n    const t = now || this.now();\n\n    // params\n    if (this.frameId <= 1) {\n      this.paramFrame = this.frameId;\n      this.paramTime = t;\n    } else {\n      const duration = t - this.paramTime;\n      if (duration >= 1e3) {\n        const frameCount = this.frameId - this.paramFrame;\n        const fps = frameCount / duration * 1e3;\n        for (let i = 0; i < this.names.length; i++) {\n          const cpu = this.cpuAccums[i] / duration * 100;\n          const gpu = this.gpuAccums[i] / duration * 100;\n          const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;\n          this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);\n          this.cpuAccums[i] = 0;\n          Promise.all(this.finished).then(() => {\n            this.gpuAccums[i] = 0;\n            this.finished = [];\n          });\n        }\n        this.paramFrame = this.frameId;\n        this.paramTime = t;\n      }\n    }\n\n    // chart\n    if (!this.detected || !this.chartFrame) {\n      this.chartFrame = this.frameId;\n      this.chartTime = t;\n      this.circularId = 0;\n    } else {\n      const timespan = t - this.chartTime;\n      let hz = this.chartHz * timespan / 1e3;\n      while (--hz > 0 && this.detected) {\n        const frameCount = this.frameId - this.chartFrame;\n        const fps = frameCount / timespan * 1e3;\n        this.chart[this.circularId % this.chartLen] = fps;\n        for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);\n        this.circularId++;\n        this.chartFrame = this.frameId;\n        this.chartTime = t;\n      }\n    }\n  }\n\n  /**\n   * Begin named measurement\n   * @param { string | undefined } name\n   */\n  begin(name) {\n    this.updateAccums(name);\n  }\n\n  /**\n   * End named measure\n   * @param { string | undefined } name\n   */\n  end(name) {\n    this.updateAccums(name);\n  }\n\n  updateAccums(name) {\n    let nameId = this.names.indexOf(name);\n    if (nameId === -1) {\n      nameId = this.names.length;\n      this.addUI(name);\n    }\n\n    const t = this.now();\n    const dt = t - this.t0;\n    for (let i = 0; i < nameId + 1; i++) {\n      if (this.activeAccums[i]) this.cpuAccums[i] += dt;\n    }\n    this.activeAccums[nameId] = !this.activeAccums[nameId];\n    this.t0 = t;\n  }\n}\n\nexport default GLBench;\n", "// import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human\nimport Human from '../src/human';\nimport Menu from './menu.js';\nimport GLBench from './gl-bench.js';\n\n// const userConfig = { backend: 'webgl' }; // add any user configuration overrides\n\nconst userConfig = {\n  backend: 'webgl',\n  async: false,\n  warmup: 'full',\n  videoOptimized: true,\n  filter: { enabled: true },\n  face: { enabled: false, mesh: { enabled: false }, iris: { enabled: false }, age: { enabled: false }, gender: { enabled: false }, emotion: { enabled: false }, embedding: { enabled: false } },\n  hand: { enabled: false },\n  gesture: { enabled: false },\n  body: { enabled: true, modelPath: '../models/efficientpose.json' },\n  object: { enabled: false },\n};\n\nconst human = new Human(userConfig);\n\n// ui options\nconst ui = {\n  baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'\n  crop: true, // video mode crop to size or leave full frame\n  columns: 2, // when processing sample images create this many columns\n  facing: true, // camera facing front or back\n  useWorker: false, // use web workers for processing\n  worker: 'worker.js',\n  samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],\n  compare: '../assets/sample-me.jpg',\n  console: true, // log messages to browser console\n  maxFPSframes: 10, // keep fps history for how many frames\n  modelsPreload: true, // preload human models on startup\n  busy: false, // internal camera busy flag\n  menuWidth: 0, // internal\n  menuHeight: 0, // internal\n  camera: {}, // internal, holds details of webcam details\n  detectFPS: [], // internal, holds fps values for detection performance\n  drawFPS: [], // internal, holds fps values for draw performance\n  buffered: false, // experimental, should output be buffered between frames\n  drawWarmup: false, // debug only, should warmup image processing be displayed on startup\n  drawThread: null, // internl, perform draw operations in a separate thread\n  detectThread: null, // internl, perform detect operations in a separate thread\n  framesDraw: 0, // internal, statistics on frames drawn\n  framesDetect: 0, // internal, statistics on frames detected\n  bench: true, // show gl fps benchmark window\n  lastFrame: 0, // time of last frame processing\n};\n\n// global variables\nconst menu = {};\nlet worker;\nlet bench;\nlet lastDetectedResult = {};\n\n// helper function: translates json to human readable string\nfunction str(...msg) {\n  if (!Array.isArray(msg)) return msg;\n  let line = '';\n  for (const entry of msg) {\n    if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ', ');\n    else line += entry;\n  }\n  return line;\n}\n\n// helper function: wrapper around console output\nfunction log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (ui.console) console.log(ts, ...msg);\n}\n\nfunction status(msg) {\n  // eslint-disable-next-line no-console\n  const div = document.getElementById('status');\n  if (div) div.innerText = msg;\n}\n\nconst compare = { enabled: false, original: null };\nasync function calcSimmilariry(result) {\n  document.getElementById('compare-container').style.display = compare.enabled ? 'block' : 'none';\n  if (!compare.enabled) return;\n  if (!(result?.face?.length > 0) || (result?.face[0]?.embedding?.length <= 64)) return;\n  if (!compare.original) {\n    compare.original = result;\n    log('setting face compare baseline:', result.face[0]);\n    if (result.face[0].tensor) {\n      const enhanced = human.enhance(result.face[0]);\n      if (enhanced) {\n        const c = document.getElementById('orig');\n        const squeeze = enhanced.squeeze();\n        const norm = squeeze.div(255);\n        human.tf.browser.toPixels(norm, c);\n        enhanced.dispose();\n        squeeze.dispose();\n        norm.dispose();\n      }\n    } else {\n      document.getElementById('compare-canvas').getContext('2d').drawImage(compare.original.canvas, 0, 0, 200, 200);\n    }\n  }\n  const similarity = human.similarity(compare.original?.face[0]?.embedding, result?.face[0]?.embedding);\n  document.getElementById('similarity').innerText = `similarity: ${Math.trunc(1000 * similarity) / 10}%`;\n}\n\n// draws processed results and starts processing of a next frame\nlet lastDraw = performance.now();\nasync function drawResults(input) {\n  const result = lastDetectedResult;\n  const canvas = document.getElementById('canvas');\n\n  // update draw fps data\n  ui.drawFPS.push(1000 / (performance.now() - lastDraw));\n  if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();\n  lastDraw = performance.now();\n\n  // draw fps chart\n  await menu.process.updateChart('FPS', ui.detectFPS);\n\n  // get updated canvas\n  if (ui.buffered || !result.canvas) {\n    const image = await human.image(input);\n    result.canvas = image.canvas;\n    human.tf.dispose(image.tensor);\n  }\n\n  // draw image from video\n  const ctx = canvas.getContext('2d');\n  ctx.fillStyle = ui.baseBackground;\n  ctx.fillRect(0, 0, canvas.width, canvas.height);\n  if (result.canvas) {\n    if (result.canvas.width !== canvas.width) canvas.width = result.canvas.width;\n    if (result.canvas.height !== canvas.height) canvas.height = result.canvas.height;\n    ctx.drawImage(result.canvas, 0, 0, result.canvas.width, result.canvas.height, 0, 0, result.canvas.width, result.canvas.height);\n  } else {\n    ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n  }\n\n  // draw all results\n  human.draw.face(canvas, result.face);\n  human.draw.body(canvas, result.body);\n  human.draw.hand(canvas, result.hand);\n  human.draw.object(canvas, result.object);\n  human.draw.gesture(canvas, result.gesture);\n  await calcSimmilariry(result);\n\n  // update log\n  const engine = human.tf.engine();\n  const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';\n  const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;\n  const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';\n  const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;\n  const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;\n  const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? '<font color=\"lightcoral\">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>' : '';\n  document.getElementById('log').innerHTML = `\n    video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}<br>\n    backend: ${human.tf.getBackend()} | ${memory}<br>\n    performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}<br>\n    ${warning}<br>\n  `;\n\n  ui.framesDraw++;\n  ui.lastFrame = performance.now();\n  // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded\n  if (ui.buffered) {\n    ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));\n  } else if (!ui.buffered && ui.drawThread) {\n    log('stopping buffered refresh');\n    cancelAnimationFrame(ui.drawThread);\n    ui.drawThread = null;\n  }\n}\n\n// setup webcam\nasync function setupCamera() {\n  if (ui.busy) return null;\n  ui.busy = true;\n  const video = document.getElementById('video');\n  const canvas = document.getElementById('canvas');\n  const output = document.getElementById('log');\n  const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;\n  let msg = '';\n  status('setting up camera');\n  // setup webcam. note that navigator.mediaDevices requires that page is accessed via https\n  if (!navigator.mediaDevices) {\n    msg = 'camera access not supported';\n    output.innerText += `\\n${msg}`;\n    log(msg);\n    status(msg);\n    ui.busy = false;\n    return msg;\n  }\n  let stream;\n  const constraints = {\n    audio: false,\n    video: { facingMode: ui.facing ? 'user' : 'environment', resizeMode: ui.crop ? 'crop-and-scale' : 'none' },\n  };\n  if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };\n  else constraints.video.height = { ideal: (window.innerHeight - document.getElementById('menubar').offsetHeight) };\n  try {\n    stream = await navigator.mediaDevices.getUserMedia(constraints);\n  } catch (err) {\n    if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';\n    else if (err.name === 'SourceUnavailableError') msg = 'camera not available';\n    else msg = `camera error: ${err.message || err}`;\n    output.innerText += `\\n${msg}`;\n    status(msg);\n    log('camera error:', err);\n    ui.busy = false;\n    return msg;\n  }\n  if (stream) video.srcObject = stream;\n  else {\n    ui.busy = false;\n    return 'camera stream empty';\n  }\n  const track = stream.getVideoTracks()[0];\n  const settings = track.getSettings();\n  // log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);\n  ui.camera = { name: track.label?.toLowerCase(), width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };\n  return new Promise((resolve) => {\n    video.onloadeddata = async () => {\n      video.width = video.videoWidth;\n      video.height = video.videoHeight;\n      canvas.width = video.width;\n      canvas.height = video.height;\n      canvas.style.width = canvas.width > canvas.height ? '100vw' : '';\n      canvas.style.height = canvas.width > canvas.height ? '' : '100vh';\n      ui.menuWidth.input.setAttribute('value', video.width);\n      ui.menuHeight.input.setAttribute('value', video.height);\n      // silly font resizing for paint-on-canvas since viewport can be zoomed\n      if (live) video.play();\n      // eslint-disable-next-line no-use-before-define\n      if (live && !ui.detectThread) runHumanDetect(video, canvas);\n      ui.busy = false;\n      // do once more because onresize events can be delayed or skipped\n      // if (video.width > window.innerWidth) await setupCamera();\n      status('');\n      resolve();\n    };\n  });\n}\n\nfunction initPerfMonitor() {\n  if (!bench) {\n    const gl = null;\n    // cosnt gl = human.tf.engine().backend.gpgpu.gl;\n    // if (!gl) log('bench cannot get tensorflow webgl context');\n    bench = new GLBench(gl, {\n      trackGPU: false, // this is really slow\n      chartHz: 20,\n      chartLen: 20,\n    });\n    bench.begin();\n  }\n}\n\n// wrapper for worker.postmessage that creates worker if one does not exist\nfunction webWorker(input, image, canvas, timestamp) {\n  if (!worker) {\n    // create new webworker and add event handler only once\n    log('creating worker thread');\n    worker = new Worker(ui.worker, { type: 'module' });\n    // after receiving message from webworker, parse&draw results and send new frame for processing\n    worker.addEventListener('message', (msg) => {\n      if (msg.data.result.performance && msg.data.result.performance.total) ui.detectFPS.push(1000 / msg.data.result.performance.total);\n      if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n      if (ui.bench) {\n        if (!bench) initPerfMonitor();\n        bench.nextFrame(timestamp);\n      }\n      if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n      lastDetectedResult = msg.data.result;\n      ui.framesDetect++;\n      if (!ui.drawThread) drawResults(input);\n      // eslint-disable-next-line no-use-before-define\n      ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n    });\n  }\n  // pass image data as arraybuffer to worker by reference to avoid copy\n  worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, userConfig }, [image.data.buffer]);\n}\n\n// main processing function when input is webcam, can use direct invocation or web worker\nfunction runHumanDetect(input, canvas, timestamp) {\n  // if live video\n  const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);\n  if (!live && input.srcObject) {\n    // stop ui refresh\n    if (ui.drawThread) cancelAnimationFrame(ui.drawThread);\n    if (ui.detectThread) cancelAnimationFrame(ui.detectThread);\n    ui.drawThread = null;\n    ui.detectThread = null;\n    // if we want to continue and camera not ready, retry in 0.5sec, else just give up\n    if (input.paused) log('camera paused');\n    else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);\n    else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);\n    clearTimeout(ui.drawThread);\n    ui.drawThread = null;\n    log('frame statistics: process:', ui.framesDetect, 'refresh:', ui.framesDraw);\n    log('memory', human.tf.engine().memory());\n    return;\n  }\n  status('');\n  if (ui.useWorker) {\n    // get image data from video as we cannot send html objects to webworker\n    const offscreen = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(canvas.width, canvas.height) : document.createElement('canvas');\n    offscreen.width = canvas.width;\n    offscreen.height = canvas.height;\n    const ctx = offscreen.getContext('2d');\n    ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n    const data = ctx.getImageData(0, 0, canvas.width, canvas.height);\n    // perform detection in worker\n    webWorker(input, data, canvas, userConfig, timestamp);\n  } else {\n    human.detect(input, userConfig).then((result) => {\n      if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);\n      if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n      if (ui.bench) {\n        if (!bench) initPerfMonitor();\n        bench.nextFrame(timestamp);\n      }\n      if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n      if (result.error) {\n        log(result.error);\n        document.getElementById('log').innerText += `\\nHuman error: ${result.error}`;\n      } else {\n        lastDetectedResult = result;\n        if (!ui.drawThread) drawResults(input);\n        ui.framesDetect++;\n        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n      }\n    });\n  }\n}\n\n// main processing function when input is image, can use direct invocation or web worker\nasync function processImage(input) {\n  return new Promise((resolve) => {\n    const image = new Image();\n    image.onload = async () => {\n      log('processing image:', encodeURI(image.src));\n      const canvas = document.getElementById('canvas');\n      image.width = image.naturalWidth;\n      image.height = image.naturalHeight;\n      canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;\n      canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;\n      const result = await human.detect(image, userConfig);\n      lastDetectedResult = result;\n      await drawResults(image);\n      const thumb = document.createElement('canvas');\n      thumb.className = 'thumbnail';\n      thumb.width = window.innerWidth / (ui.columns + 0.1);\n      thumb.height = thumb.width * canvas.height / canvas.width;\n      if (result.face && result.face.length > 0) {\n        thumb.title = result.face.map((a, i) => `#${i} face: ${Math.trunc(100 * a.faceConfidence)}% box: ${Math.trunc(100 * a.boxConfidence)}% age: ${Math.trunc(a.age)} gender: ${Math.trunc(100 * a.genderConfidence)}% ${a.gender}`).join(' | ');\n      } else {\n        thumb.title = 'no face detected';\n      }\n      const ctx = thumb.getContext('2d');\n      ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);\n      document.getElementById('samples-container').appendChild(thumb);\n      image.src = '';\n      resolve(true);\n    };\n    image.src = input;\n  });\n}\n\n// just initialize everything and call main function\nasync function detectVideo() {\n  document.getElementById('samples-container').style.display = 'none';\n  document.getElementById('canvas').style.display = 'block';\n  const video = document.getElementById('video');\n  const canvas = document.getElementById('canvas');\n  if ((video.srcObject !== null) && !video.paused) {\n    document.getElementById('play').style.display = 'block';\n    document.getElementById('btnStart').className = 'button button-start';\n    document.getElementById('btnStart').innerHTML = 'start<br>video';\n    status('paused');\n    video.pause();\n  } else {\n    const cameraError = await setupCamera();\n    if (!cameraError) {\n      document.getElementById('play').style.display = 'none';\n      for (const m of Object.values(menu)) m.hide();\n      status('');\n      document.getElementById('btnStart').className = 'button button-stop';\n      document.getElementById('btnStart').innerHTML = 'pause<br>video';\n      await video.play();\n      if (!ui.detectThread) runHumanDetect(video, canvas);\n    } else {\n      status(cameraError);\n    }\n  }\n}\n\n// just initialize everything and call main function\nasync function detectSampleImages() {\n  userConfig.videoOptimized = false; // force disable video optimizations\n  document.getElementById('play').style.display = 'none';\n  document.getElementById('canvas').style.display = 'none';\n  document.getElementById('samples-container').style.display = 'block';\n  log('running detection of sample images');\n  status('processing images');\n  document.getElementById('samples-container').innerHTML = '';\n  for (const m of Object.values(menu)) m.hide();\n  for (const image of ui.samples) await processImage(image);\n  status('');\n}\n\nfunction setupMenu() {\n  let x = [];\n  if (window.innerWidth > 800) {\n    // initial position of menu items, later it's calculated based on mouse coordinates\n    x = [`${document.getElementById('btnDisplay').offsetLeft - 50}px`, `${document.getElementById('btnImage').offsetLeft - 50}px`, `${document.getElementById('btnProcess').offsetLeft - 50}px`, `${document.getElementById('btnModel').offsetLeft - 50}px`];\n  } else {\n    // absolute minimum spacing for menus\n    x = ['0rem', '11rem', '21.1rem', '33rem'];\n  }\n\n  menu.display = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[0] });\n  menu.display.addBool('perf monitor', ui, 'bench', (val) => ui.bench = val);\n  menu.display.addBool('buffered output', ui, 'buffered', (val) => ui.buffered = val);\n  menu.display.addBool('crop & scale', ui, 'crop', (val) => {\n    ui.crop = val;\n    setupCamera();\n  });\n  menu.display.addBool('camera facing', ui, 'facing', (val) => {\n    ui.facing = val;\n    setupCamera();\n  });\n  menu.display.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.display.addBool('use 3D depth', human.draw.drawOptions, 'useDepth');\n  menu.display.addBool('draw with curves', human.draw.drawOptions, 'useCurves');\n  menu.display.addBool('print labels', human.draw.drawOptions, 'drawLabels');\n  menu.display.addBool('draw points', human.draw.drawOptions, 'drawPoints');\n  menu.display.addBool('draw boxes', human.draw.drawOptions, 'drawBoxes');\n  menu.display.addBool('draw polygons', human.draw.drawOptions, 'drawPolygons');\n  menu.display.addBool('fill polygons', human.draw.drawOptions, 'fillPolygons');\n\n  menu.image = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[1] });\n  menu.image.addBool('enabled', human.config.filter, 'enabled', (val) => human.config.filter.enabled = val);\n  ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));\n  ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));\n  menu.image.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));\n  menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));\n  menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));\n  menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));\n  menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));\n  menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));\n  menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));\n  menu.image.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.image.addBool('negative', human.config.filter, 'negative', (val) => human.config.filter.negative = val);\n  menu.image.addBool('sepia', human.config.filter, 'sepia', (val) => human.config.filter.sepia = val);\n  menu.image.addBool('vintage', human.config.filter, 'vintage', (val) => human.config.filter.vintage = val);\n  menu.image.addBool('kodachrome', human.config.filter, 'kodachrome', (val) => human.config.filter.kodachrome = val);\n  menu.image.addBool('technicolor', human.config.filter, 'technicolor', (val) => human.config.filter.technicolor = val);\n  menu.image.addBool('polaroid', human.config.filter, 'polaroid', (val) => human.config.filter.polaroid = val);\n\n  menu.process = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[2] });\n  menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);\n  menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);\n  // menu.process.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);\n  // menu.process.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);\n  menu.process.addBool('use web worker', ui, 'useWorker');\n  menu.process.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.process.addLabel('model parameters');\n  menu.process.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {\n    human.config.face.detector.maxFaces = parseInt(val);\n    human.config.body.maxDetections = parseInt(val);\n    human.config.hand.maxHands = parseInt(val);\n  });\n  menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {\n    human.config.face.detector.skipFrames = parseInt(val);\n    human.config.face.emotion.skipFrames = parseInt(val);\n    human.config.face.age.skipFrames = parseInt(val);\n    human.config.hand.skipFrames = parseInt(val);\n  });\n  menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {\n    human.config.face.detector.minConfidence = parseFloat(val);\n    human.config.face.gender.minConfidence = parseFloat(val);\n    human.config.face.emotion.minConfidence = parseFloat(val);\n    human.config.hand.minConfidence = parseFloat(val);\n  });\n  menu.process.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {\n    human.config.face.detector.scoreThreshold = parseFloat(val);\n    human.config.hand.scoreThreshold = parseFloat(val);\n    human.config.body.scoreThreshold = parseFloat(val);\n  });\n  menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {\n    human.config.face.detector.iouThreshold = parseFloat(val);\n    human.config.hand.iouThreshold = parseFloat(val);\n  });\n  menu.process.addBool('detection rotation', human.config.face.detector, 'rotation', (val) => {\n    human.config.face.detector.rotation = val;\n    human.config.hand.rotation = val;\n  });\n  menu.process.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.process.addButton('process sample images', 'process images', () => detectSampleImages());\n  menu.process.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.process.addChart('FPS', 'FPS');\n\n  menu.models = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[3] });\n  menu.models.addBool('face detect', human.config.face, 'enabled', (val) => human.config.face.enabled = val);\n  menu.models.addBool('face mesh', human.config.face.mesh, 'enabled', (val) => human.config.face.mesh.enabled = val);\n  menu.models.addBool('face iris', human.config.face.iris, 'enabled', (val) => human.config.face.iris.enabled = val);\n  menu.models.addBool('face description', human.config.face.description, 'enabled', (val) => human.config.face.age.description = val);\n  // menu.models.addBool('face age', human.config.face.age, 'enabled', (val) => human.config.face.age.enabled = val);\n  // menu.models.addBool('face gender', human.config.face.gender, 'enabled', (val) => human.config.face.gender.enabled = val);\n  menu.models.addBool('face emotion', human.config.face.emotion, 'enabled', (val) => human.config.face.emotion.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('body pose', human.config.body, 'enabled', (val) => human.config.body.enabled = val);\n  menu.models.addBool('hand pose', human.config.hand, 'enabled', (val) => human.config.hand.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('gestures', human.config.gesture, 'enabled', (val) => human.config.gesture.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('object detection', human.config.object, 'enabled', (val) => human.config.object.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('face compare', compare, 'enabled', (val) => {\n    compare.enabled = val;\n    compare.original = null;\n  });\n\n  document.getElementById('btnDisplay').addEventListener('click', (evt) => menu.display.toggle(evt));\n  document.getElementById('btnImage').addEventListener('click', (evt) => menu.image.toggle(evt));\n  document.getElementById('btnProcess').addEventListener('click', (evt) => menu.process.toggle(evt));\n  document.getElementById('btnModel').addEventListener('click', (evt) => menu.models.toggle(evt));\n  document.getElementById('btnStart').addEventListener('click', () => detectVideo());\n  document.getElementById('play').addEventListener('click', () => detectVideo());\n}\n\nasync function drawWarmup(res) {\n  const canvas = document.getElementById('canvas');\n  canvas.width = res.canvas.width;\n  canvas.height = res.canvas.height;\n  const ctx = canvas.getContext('2d');\n  ctx.drawImage(res.canvas, 0, 0, res.canvas.width, res.canvas.height, 0, 0, canvas.width, canvas.height);\n  await human.draw.all(canvas, res);\n}\n\nasync function main() {\n  log('demo starting ...');\n  setupMenu();\n  document.getElementById('log').innerText = `Human: version ${human.version}`;\n  if (ui.modelsPreload && !ui.useWorker) {\n    status('loading');\n    await human.load(userConfig); // this is not required, just pre-loads all models\n    const loaded = Object.keys(human.models).filter((a) => human.models[a]);\n    log('demo loaded models:', loaded);\n  }\n  if (!ui.useWorker) {\n    status('initializing');\n    const res = await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference\n    if (res && res.canvas && ui.drawWarmup) await drawWarmup(res);\n  }\n  status('human: ready');\n  document.getElementById('loader').style.display = 'none';\n  document.getElementById('play').style.display = 'block';\n  log('demo ready...');\n}\n\nwindow.onload = main;\nwindow.onresize = setupCamera;\n"],
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+  "sourcesContent": ["// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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_ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var 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_OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var 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___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=function(){return(___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=Module[\"asm\"][\"db\"]).apply(null,arguments)};var _emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=function(){return(_emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=Module[\"asm\"][\"eb\"]).apply(null,arguments)};var _emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=function(){return(_emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=Module[\"asm\"][\"fb\"]).apply(null,arguments)};var _emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n  return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return 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Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). 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fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return 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false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    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y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.executeAsync(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      this.tf.enableDebugMode();\n      */\n      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "let instance = 0;\nlet CSScreated = false;\n\nlet theme = {\n  background: 'darkslategray',\n  hover: 'lightgray',\n  itemBackground: 'black',\n  itemColor: 'white',\n  buttonBackground: 'lightblue',\n  buttonHover: 'lightgreen',\n  checkboxOn: 'lightgreen',\n  checkboxOff: 'lightcoral',\n  rangeBackground: 'lightblue',\n  rangeLabel: 'white',\n  chartColor: 'lightblue',\n};\n\nfunction createCSS() {\n  if (CSScreated) return;\n  const css = `\n  :root { --rounded: 0.1rem; }\n  .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;\n          box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }\n\n  .menu:hover { box-shadow: 0 0 8px ${theme.hover}; }\n  .menu-container { display: block; max-height: 100vh; }\n  .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }\n  .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }\n  .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }\n  .menu-title { cursor: pointer; }\n  .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }\n  .menu-label { padding: 0; font-weight: 800; }\n\n  .menu-list { margin-right: 0.8rem; }\n  select:focus { outline: none; }\n  .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;\n    font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }\n\n  .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}\n  .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }\n  \n  .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;\n    border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }\n  .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }\n  .menu-button:focus { outline: none; }\n\n  .menu-checkbox { width: 2.8rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }\n  .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n  .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n  .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${theme.checkboxOff};\n    border-radius: var(--rounded); transition: left 0.6s ease; }\n\n  input[type=checkbox] { visibility: hidden; }\n  input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }\n\n  .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${theme.rangeBackground}; }\n  .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }\n\n  input[type=range] { -webkit-appearance: none; }\n  input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n  input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n  input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n  input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n\n  .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }\n  .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }\n  `;\n  const el = document.createElement('style');\n  el.innerHTML = css;\n  document.getElementsByTagName('head')[0].appendChild(el);\n  CSScreated = true;\n}\n\nclass Menu {\n  constructor(parent, title, position, userTheme) {\n    if (userTheme) theme = { ...theme, ...userTheme };\n    createCSS();\n    this.createMenu(parent, title, position);\n    this.id = 0;\n    this.instance = instance;\n    instance++;\n    this._maxFPS = 0;\n    this.hidden = 0;\n  }\n\n  createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {\n    this.menu = document.createElement('div');\n    this.menu.id = `menu-${instance}`;\n    this.menu.className = 'menu';\n    if (position) {\n      if (position.top) this.menu.style.top = position.top;\n      if (position.bottom) this.menu.style.bottom = position.bottom;\n      if (position.left) this.menu.style.left = position.left;\n      if (position.right) this.menu.style.right = position.right;\n    }\n\n    this.container = document.createElement('div');\n    this.container.id = `menu-container-${instance}`;\n    this.container.className = 'menu-container menu-container-fadein';\n\n    // set menu title with pulldown arrow\n    const elTitle = document.createElement('div');\n    elTitle.className = 'menu-title';\n    elTitle.id = `menu-title-${instance}`;\n    const svg = `<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 448 512\" style=\"width: 2rem; height: 2rem; vertical-align: top;\">\n        <path d=\"M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h352a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48zm-51.37 182.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z\" class=\"svg-background\"/>\n        <path d=\"M348.63 214.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z\" class=\"svg-foreground\"/>\n      </svg>`;\n    if (title) elTitle.innerHTML = `${title}${svg}`;\n    this.menu.appendChild(elTitle);\n    elTitle.addEventListener('click', () => {\n      this.container.classList.toggle('menu-container-fadeout');\n      this.container.classList.toggle('menu-container-fadein');\n      this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';\n    });\n\n    this.menu.appendChild(this.container);\n    if (typeof parent === 'object') parent.appendChild(this.menu);\n    else document.getElementById(parent).appendChild(this.menu);\n  }\n\n  get newID() {\n    this.id++;\n    return `menu-${this.instance}-${this.id}`;\n  }\n\n  get ID() {\n    return `menu-${this.instance}-${this.id}`;\n  }\n\n  get width() {\n    return this.menu.offsetWidth;\n  }\n\n  get height() {\n    return this.menu.offsetHeight;\n  }\n\n  hide() {\n    if (this.container.classList.contains('menu-container-fadein')) {\n      this.container.classList.toggle('menu-container-fadeout');\n      this.container.classList.toggle('menu-container-fadein');\n    }\n  }\n\n  visible() {\n    return (this.container.classList.contains('menu-container-fadein'));\n  }\n\n  toggle(evt) {\n    this.container.classList.toggle('menu-container-fadeout');\n    this.container.classList.toggle('menu-container-fadein');\n    if (this.container.classList.contains('menu-container-fadein') && evt) {\n      const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);\n      // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);\n      if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;\n      // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;\n      if (this.menu.offsetLeft < 0) this.menu.style.left = 0;\n      if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {\n        this.menu.style.left = null;\n        this.menu.style.right = 0;\n      }\n      this.menu.style.borderStyle = 'solid';\n    } else {\n      this.menu.style.borderStyle = 'none';\n    }\n  }\n\n  addTitle(title) {\n    const el = document.createElement('div');\n    el.className = 'menu-title';\n    el.id = this.newID;\n    el.innerHTML = title;\n    this.menu.appendChild(el);\n    el.addEventListener('click', () => {\n      this.hidden = !this.hidden;\n      const all = document.getElementsByClassName('menu');\n      for (const item of all) {\n        item.style.display = this.hidden ? 'none' : 'block';\n      }\n    });\n    return el;\n  }\n\n  addLabel(title) {\n    const el = document.createElement('div');\n    el.className = 'menu-item menu-label';\n    el.id = this.newID;\n    el.innerHTML = title;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  addBool(title, object, variable, callback) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.innerHTML = `<div class=\"menu-checkbox\"><input class=\"menu-checkbox\" type=\"checkbox\" id=\"${this.newID}\" ${object[variable] ? 'checked' : ''}/><label class=\"menu-checkbox-label\" for=\"${this.ID}\"></label></div>${title}`;\n    this.container.appendChild(el);\n    el.addEventListener('change', (evt) => {\n      object[variable] = evt.target.checked;\n      if (callback) callback(evt.target.checked);\n    });\n    return el;\n  }\n\n  async addList(title, items, selected, callback) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    let options = '';\n    for (const item of items) {\n      const def = item === selected ? 'selected' : '';\n      options += `<option value=\"${item}\" ${def}>${item}</option>`;\n    }\n    el.innerHTML = `<div class=\"menu-list\"><select name=\"${this.ID}\" class=\"menu-list-item\">${options}</select><label for=\"${this.ID}\"></label></div>${title}`;\n    el.style.fontFamily = document.body.style.fontFamily;\n    el.style.fontSize = document.body.style.fontSize;\n    el.style.fontVariant = document.body.style.fontVariant;\n    this.container.appendChild(el);\n    el.addEventListener('change', (evt) => {\n      if (callback) callback(items[evt.target.selectedIndex]);\n    });\n    return el;\n  }\n\n  addRange(title, object, variable, min, max, step, callback) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.innerHTML = `<input class=\"menu-range\" type=\"range\" id=\"${this.newID}\" min=\"${min}\" max=\"${max}\" step=\"${step}\" value=\"${object[variable]}\">${title}`;\n    this.container.appendChild(el);\n    el.addEventListener('change', (evt) => {\n      object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);\n      evt.target.setAttribute('value', evt.target.value);\n      if (callback) callback(evt.target.value);\n    });\n    el.input = el.children[0];\n    return el;\n  }\n\n  addHTML(html) {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.id = this.newID;\n    if (html) el.innerHTML = html;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  addButton(titleOn, titleOff, callback) {\n    const el = document.createElement('button');\n    el.className = 'menu-item menu-button';\n    el.style.fontFamily = document.body.style.fontFamily;\n    el.style.fontSize = document.body.style.fontSize;\n    el.style.fontVariant = document.body.style.fontVariant;\n    el.type = 'button';\n    el.id = this.newID;\n    el.innerText = titleOn;\n    this.container.appendChild(el);\n    el.addEventListener('click', () => {\n      if (el.innerText === titleOn) el.innerText = titleOff;\n      else el.innerText = titleOn;\n      if (callback) callback(el.innerText !== titleOn);\n    });\n    return el;\n  }\n\n  addValue(title, val, suffix = '') {\n    const el = document.createElement('div');\n    el.className = 'menu-item';\n    el.id = `menu-val-${title}`;\n    el.innerText = `${title}: ${val}${suffix}`;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  updateValue(title, val, suffix = '') {\n    const el = document.getElementById(`menu-val-${title}`);\n    if (el) el.innerText = `${title}: ${val}${suffix}`;\n    else this.addValue(title, val);\n  }\n\n  addChart(title, id, width = 150, height = 40, color) {\n    if (color) theme.chartColor = color;\n    const el = document.createElement('div');\n    el.className = 'menu-item menu-chart-title';\n    el.id = this.newID;\n    el.innerHTML = `<font color=${theme.chartColor}>${title}</font><canvas id=\"menu-canvas-${id}\" class=\"menu-chart-canvas\" width=\"${width}px\" height=\"${height}px\"></canvas>`;\n    this.container.appendChild(el);\n    return el;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  async updateChart(id, values) {\n    if (!values || (values.length === 0)) return;\n    const canvas = document.getElementById(`menu-canvas-${id}`);\n    if (!canvas) return;\n    const ctx = canvas.getContext('2d');\n    ctx.fillStyle = theme.background;\n    ctx.fillRect(0, 0, canvas.width, canvas.height);\n    const width = canvas.width / values.length;\n    const max = 1 + Math.max(...values);\n    const height = canvas.height / max;\n    for (let i = 0; i < values.length; i++) {\n      const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);\n      gradient.addColorStop(0.1, theme.chartColor);\n      gradient.addColorStop(0.4, theme.background);\n      ctx.fillStyle = gradient;\n      ctx.fillRect(i * width, 0, width - 4, canvas.height);\n      ctx.fillStyle = theme.background;\n      ctx.font = `${width / 1.5}px \"Segoe UI\"`;\n      ctx.fillText(Math.round(values[i]), i * width + 1, canvas.height - 1, width - 1);\n    }\n  }\n}\n\nexport default Menu;\n", "/* eslint-disable max-len */\n\n// based on: https://github.com/munrocket/gl-bench\n\nconst UICSS = `\n  #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }\n  #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 2px 0 2px; background: darkslategray; border-radius: 0.1rem; cursor: pointer; opacity: 0.9; }\n  #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }\n  #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }\n  #gl-bench .gl-mem { font-size: 12px; fill: white; }\n  #gl-bench .gl-fps { font-size: 13px; fill: white; }\n  #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }\n  #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }\n  #gl-bench rect { fill: black; }\n  #gl-bench .opacity { stroke: black; }\n  `;\n\nconst UISVG = `\n  <div class=\"gl-box\">\n    <svg viewBox=\"0 0 60 60\">\n      <text x=\"27\" y=\"56\" class=\"gl-fps\">00 FPS</text>\n      <text x=\"30\" y=\"8\" class=\"gl-mem\"></text>\n      <rect x=\"0\" y=\"14\" rx=\"4\" ry=\"4\" width=\"60\" height=\"32\"></rect>\n      <polyline class=\"gl-chart\"></polyline>\n    </svg>\n    <svg viewBox=\"0 0 14 60\" class=\"gl-cpu-svg\">\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"opacity\"/>\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"gl-cpu\" stroke-dasharray=\"0 27\"/>\n      <path d=\"M5.35 43c-.464 0-.812.377-.812.812v1.16c-.783.1972-1.421.812-1.595 1.624h-1.16c-.435 0-.812.348-.812.812s.348.812.812.812h1.102v1.653H1.812c-.464 0-.812.377-.812.812 0 .464.377.812.812.812h1.131c.1943.783.812 1.392 1.595 1.595v1.131c0 .464.377.812.812.812.464 0 .812-.377.812-.812V53.15h1.653v1.073c0 .464.377.812.812.812.464 0 .812-.377.812-.812v-1.131c.783-.1943 1.392-.812 1.595-1.595h1.131c.464 0 .812-.377.812-.812 0-.464-.377-.812-.812-.812h-1.073V48.22h1.102c.435 0 .812-.348.812-.812s-.348-.812-.812-.812h-1.16c-.1885-.783-.812-1.421-1.595-1.624v-1.131c0-.464-.377-.812-.812-.812-.464 0-.812.377-.812.812v1.073H6.162v-1.073c0-.464-.377-.812-.812-.812zm.58 3.48h2.088c.754 0 1.363.609 1.363 1.363v2.088c0 .754-.609 1.363-1.363 1.363H5.93c-.754 0-1.363-.609-1.363-1.363v-2.088c0-.754.609-1.363 1.363-1.363z\" style=\"fill: grey\"></path>\n    </svg>\n    <svg viewBox=\"0 0 14 60\" class=\"gl-gpu-svg\">\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"opacity\"/>\n      <line x1=\"7\" y1=\"38\" x2=\"7\" y2=\"11\" class=\"gl-gpu\" stroke-dasharray=\"0 27\"/>\n      <path d=\"M1.94775 43.3772a.736.736 0 10-.00416 1.472c.58535.00231.56465.1288.6348.3197.07015.18975.04933.43585.04933.43585l-.00653.05405v8.671a.736.736 0 101.472 0v-1.4145c.253.09522.52785.1495.81765.1495h5.267c1.2535 0 2.254-.9752 2.254-2.185v-3.105c0-1.2075-1.00625-2.185-2.254-2.185h-5.267c-.28865 0-.5635.05405-.8165.1495.01806-.16445.04209-.598-.1357-1.0787-.22425-.6072-.9499-1.2765-2.0125-1.2765zm2.9095 3.6455c.42435 0 .7659.36225.7659.8119v2.9785c0 .44965-.34155.8119-.7659.8119s-.7659-.36225-.7659-.8119v-2.9785c0-.44965.34155-.8119.7659-.8119zm4.117 0a2.3 2.3 0 012.3 2.3 2.3 2.3 0 01-2.3 2.3 2.3 2.3 0 01-2.3-2.3 2.3 2.3 0 012.3-2.3z\" style=\"fill: grey\"></path>\n    </svg>\n  </div>\n  `;\n\nclass GLBench {\n  /** GLBench constructor\n   * @param { WebGLRenderingContext | WebGL2RenderingContext } gl context\n   * @param { Object | undefined } settings additional settings\n   */\n  constructor(gl, settings = {}) {\n    this.css = UICSS;\n    this.svg = UISVG;\n    // eslint-disable-next-line @typescript-eslint/no-empty-function\n    this.paramLogger = () => {};\n    // eslint-disable-next-line @typescript-eslint/no-empty-function\n    this.chartLogger = () => {};\n    this.chartLen = 20;\n    this.chartHz = 20;\n\n    this.names = [];\n    this.cpuAccums = [];\n    this.gpuAccums = [];\n    this.activeAccums = [];\n    this.chart = new Array(this.chartLen);\n    this.now = () => ((performance && performance.now) ? performance.now() : Date.now());\n    this.updateUI = () => {\n      [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');\n    };\n\n    Object.assign(this, settings);\n    this.detected = 0;\n    this.finished = [];\n    this.isFramebuffer = 0;\n    this.frameId = 0;\n\n    // 120hz device detection\n    let rafId; let n = 0; let\n      t0;\n    const loop = (t) => {\n      if (++n < 20) {\n        rafId = requestAnimationFrame(loop);\n      } else {\n        this.detected = Math.ceil(1e3 * n / (t - t0) / 70);\n        cancelAnimationFrame(rafId);\n      }\n      if (!t0) t0 = t;\n    };\n    requestAnimationFrame(loop);\n\n    // attach gpu profilers\n    if (gl) {\n      const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {\n        gl.getError();\n        const dt = this.now() - t;\n        activeAccums.forEach((active, i) => {\n          if (active) this.gpuAccums[i] += dt;\n        });\n      }, 0));\n\n      const addProfiler = (fn, self, target) => {\n        const t = self.now();\n        // eslint-disable-next-line prefer-rest-params\n        fn.apply(target, arguments);\n        if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));\n      };\n\n      /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {\n        if (gl[fn]) {\n          gl[fn] = addProfiler(gl[fn], this, gl);\n        }\n      });\n      */\n      const fn = 'drawElements';\n      if (gl[fn]) {\n        gl[fn] = addProfiler(gl[fn], this, gl);\n      } else {\n        // eslint-disable-next-line no-console\n        console.log('bench: cannot attach to webgl function');\n      }\n\n      /*\n      gl.getExtension = ((fn, self) => {\n        // eslint-disable-next-line prefer-rest-params\n        const ext = fn.apply(gl, arguments);\n        if (ext) {\n          ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {\n            if (ext[fn2]) {\n              ext[fn2] = addProfiler(ext[fn2], self, ext);\n            }\n          });\n        }\n        return ext;\n      })(gl.getExtension, this);\n      */\n    }\n\n    // init ui and ui loggers\n    if (!this.withoutUI) {\n      if (!this.dom) this.dom = document.body;\n      const elm = document.createElement('div');\n      elm.id = 'gl-bench';\n      this.dom.appendChild(elm);\n      this.dom.insertAdjacentHTML('afterbegin', '<style id=\"gl-bench-style\">' + this.css + '</style>');\n      this.dom = elm;\n      this.dom.addEventListener('click', () => {\n        this.trackGPU = !this.trackGPU;\n        this.updateUI();\n      });\n\n      this.paramLogger = ((logger, dom, names) => {\n        const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];\n        const nodes = { ...classes };\n        classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));\n        this.nodes = nodes;\n        return (i, cpu, gpu, mem, fps, totalTime, frameId) => {\n          nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';\n          nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';\n          // eslint-disable-next-line no-nested-ternary\n          nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');\n          nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);\n          logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);\n        };\n      })(this.paramLogger, this.dom, this.names);\n\n      this.chartLogger = ((logger, dom) => {\n        const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };\n        return (i, chart, circularId) => {\n          let points = '';\n          const len = chart.length;\n          for (let j = 0; j < len; j++) {\n            const id = (circularId + j + 1) % len;\n            if (chart[id] !== undefined) points = points + ' ' + (60 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 0.5 / this.detected).toFixed(1);\n          }\n          nodes['gl-chart'][i].setAttribute('points', points);\n          logger(this.names[i], chart, circularId);\n        };\n      })(this.chartLogger, this.dom);\n    }\n  }\n\n  /**\n   * Explicit UI add\n   * @param { string | undefined } name\n   */\n  addUI(name) {\n    if (this.names.indexOf(name) === -1) {\n      this.names.push(name);\n      if (this.dom) {\n        this.dom.insertAdjacentHTML('beforeend', this.svg);\n        this.updateUI();\n      }\n      this.cpuAccums.push(0);\n      this.gpuAccums.push(0);\n      this.activeAccums.push(false);\n    }\n  }\n\n  /**\n   * Increase frameID\n   * @param { number | undefined } now\n   */\n  nextFrame(now) {\n    this.frameId++;\n    const t = now || this.now();\n\n    // params\n    if (this.frameId <= 1) {\n      this.paramFrame = this.frameId;\n      this.paramTime = t;\n    } else {\n      const duration = t - this.paramTime;\n      if (duration >= 1e3) {\n        const frameCount = this.frameId - this.paramFrame;\n        const fps = frameCount / duration * 1e3;\n        for (let i = 0; i < this.names.length; i++) {\n          const cpu = this.cpuAccums[i] / duration * 100;\n          const gpu = this.gpuAccums[i] / duration * 100;\n          const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;\n          this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);\n          this.cpuAccums[i] = 0;\n          Promise.all(this.finished).then(() => {\n            this.gpuAccums[i] = 0;\n            this.finished = [];\n          });\n        }\n        this.paramFrame = this.frameId;\n        this.paramTime = t;\n      }\n    }\n\n    // chart\n    if (!this.detected || !this.chartFrame) {\n      this.chartFrame = this.frameId;\n      this.chartTime = t;\n      this.circularId = 0;\n    } else {\n      const timespan = t - this.chartTime;\n      let hz = this.chartHz * timespan / 1e3;\n      while (--hz > 0 && this.detected) {\n        const frameCount = this.frameId - this.chartFrame;\n        const fps = frameCount / timespan * 1e3;\n        this.chart[this.circularId % this.chartLen] = fps;\n        for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);\n        this.circularId++;\n        this.chartFrame = this.frameId;\n        this.chartTime = t;\n      }\n    }\n  }\n\n  /**\n   * Begin named measurement\n   * @param { string | undefined } name\n   */\n  begin(name) {\n    this.updateAccums(name);\n  }\n\n  /**\n   * End named measure\n   * @param { string | undefined } name\n   */\n  end(name) {\n    this.updateAccums(name);\n  }\n\n  updateAccums(name) {\n    let nameId = this.names.indexOf(name);\n    if (nameId === -1) {\n      nameId = this.names.length;\n      this.addUI(name);\n    }\n\n    const t = this.now();\n    const dt = t - this.t0;\n    for (let i = 0; i < nameId + 1; i++) {\n      if (this.activeAccums[i]) this.cpuAccums[i] += dt;\n    }\n    this.activeAccums[nameId] = !this.activeAccums[nameId];\n    this.t0 = t;\n  }\n}\n\nexport default GLBench;\n", "// import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human\nimport Human from '../src/human';\nimport Menu from './menu.js';\nimport GLBench from './gl-bench.js';\n\n// const userConfig = { backend: 'webgl' }; // add any user configuration overrides\n\nconst userConfig = {\n  backend: 'webgl',\n  async: false,\n  profile: false,\n  warmup: 'full',\n  videoOptimized: true,\n  filter: { enabled: true },\n  face: { enabled: true,\n    mesh: { enabled: true },\n    iris: { enabled: true },\n    description: { enabled: true },\n    emotion: { enabled: true },\n  },\n  hand: { enabled: false },\n  gesture: { enabled: false },\n  body: { enabled: false, modelPath: '../models/blazepose.json' },\n  object: { enabled: false },\n};\n\nconst human = new Human(userConfig);\n\n// ui options\nconst ui = {\n  baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'\n  crop: true, // video mode crop to size or leave full frame\n  columns: 2, // when processing sample images create this many columns\n  facing: true, // camera facing front or back\n  useWorker: false, // use web workers for processing\n  worker: 'worker.js',\n  samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],\n  compare: '../assets/sample-me.jpg',\n  console: true, // log messages to browser console\n  maxFPSframes: 10, // keep fps history for how many frames\n  modelsPreload: true, // preload human models on startup\n  busy: false, // internal camera busy flag\n  menuWidth: 0, // internal\n  menuHeight: 0, // internal\n  camera: {}, // internal, holds details of webcam details\n  detectFPS: [], // internal, holds fps values for detection performance\n  drawFPS: [], // internal, holds fps values for draw performance\n  buffered: false, // experimental, should output be buffered between frames\n  drawWarmup: false, // debug only, should warmup image processing be displayed on startup\n  drawThread: null, // internl, perform draw operations in a separate thread\n  detectThread: null, // internl, perform detect operations in a separate thread\n  framesDraw: 0, // internal, statistics on frames drawn\n  framesDetect: 0, // internal, statistics on frames detected\n  bench: true, // show gl fps benchmark window\n  lastFrame: 0, // time of last frame processing\n};\n\n// global variables\nconst menu = {};\nlet worker;\nlet bench;\nlet lastDetectedResult = {};\n\n// helper function: translates json to human readable string\nfunction str(...msg) {\n  if (!Array.isArray(msg)) return msg;\n  let line = '';\n  for (const entry of msg) {\n    if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ', ');\n    else line += entry;\n  }\n  return line;\n}\n\n// helper function: wrapper around console output\nfunction log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (ui.console) console.log(ts, ...msg);\n}\n\nfunction status(msg) {\n  // eslint-disable-next-line no-console\n  const div = document.getElementById('status');\n  if (div) div.innerText = msg;\n}\n\nconst compare = { enabled: false, original: null };\nasync function calcSimmilariry(result) {\n  document.getElementById('compare-container').style.display = compare.enabled ? 'block' : 'none';\n  if (!compare.enabled) return;\n  if (!(result?.face?.length > 0) || (result?.face[0]?.embedding?.length <= 64)) return;\n  if (!compare.original) {\n    compare.original = result;\n    log('setting face compare baseline:', result.face[0]);\n    if (result.face[0].tensor) {\n      const enhanced = human.enhance(result.face[0]);\n      if (enhanced) {\n        const c = document.getElementById('orig');\n        const squeeze = enhanced.squeeze();\n        const norm = squeeze.div(255);\n        human.tf.browser.toPixels(norm, c);\n        enhanced.dispose();\n        squeeze.dispose();\n        norm.dispose();\n      }\n    } else {\n      document.getElementById('compare-canvas').getContext('2d').drawImage(compare.original.canvas, 0, 0, 200, 200);\n    }\n  }\n  const similarity = human.similarity(compare.original?.face[0]?.embedding, result?.face[0]?.embedding);\n  document.getElementById('similarity').innerText = `similarity: ${Math.trunc(1000 * similarity) / 10}%`;\n}\n\n// draws processed results and starts processing of a next frame\nlet lastDraw = performance.now();\nasync function drawResults(input) {\n  const result = lastDetectedResult;\n  const canvas = document.getElementById('canvas');\n\n  // update draw fps data\n  ui.drawFPS.push(1000 / (performance.now() - lastDraw));\n  if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();\n  lastDraw = performance.now();\n\n  // draw fps chart\n  await menu.process.updateChart('FPS', ui.detectFPS);\n\n  // get updated canvas\n  if (ui.buffered || !result.canvas) {\n    const image = await human.image(input);\n    result.canvas = image.canvas;\n    human.tf.dispose(image.tensor);\n  }\n\n  // draw image from video\n  const ctx = canvas.getContext('2d');\n  ctx.fillStyle = ui.baseBackground;\n  ctx.fillRect(0, 0, canvas.width, canvas.height);\n  if (result.canvas) {\n    if (result.canvas.width !== canvas.width) canvas.width = result.canvas.width;\n    if (result.canvas.height !== canvas.height) canvas.height = result.canvas.height;\n    ctx.drawImage(result.canvas, 0, 0, result.canvas.width, result.canvas.height, 0, 0, result.canvas.width, result.canvas.height);\n  } else {\n    ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n  }\n\n  // draw all results\n  human.draw.face(canvas, result.face);\n  human.draw.body(canvas, result.body);\n  human.draw.hand(canvas, result.hand);\n  human.draw.object(canvas, result.object);\n  human.draw.gesture(canvas, result.gesture);\n  await calcSimmilariry(result);\n\n  // update log\n  const engine = human.tf.engine();\n  const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';\n  const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;\n  const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';\n  const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;\n  const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;\n  const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? '<font color=\"lightcoral\">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>' : '';\n  document.getElementById('log').innerHTML = `\n    video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}<br>\n    backend: ${human.tf.getBackend()} | ${memory}<br>\n    performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}<br>\n    ${warning}<br>\n  `;\n\n  ui.framesDraw++;\n  ui.lastFrame = performance.now();\n  // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded\n  if (ui.buffered) {\n    ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));\n  } else if (!ui.buffered && ui.drawThread) {\n    log('stopping buffered refresh');\n    cancelAnimationFrame(ui.drawThread);\n    ui.drawThread = null;\n  }\n}\n\n// setup webcam\nasync function setupCamera() {\n  if (ui.busy) return null;\n  ui.busy = true;\n  const video = document.getElementById('video');\n  const canvas = document.getElementById('canvas');\n  const output = document.getElementById('log');\n  const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;\n  let msg = '';\n  status('setting up camera');\n  // setup webcam. note that navigator.mediaDevices requires that page is accessed via https\n  if (!navigator.mediaDevices) {\n    msg = 'camera access not supported';\n    output.innerText += `\\n${msg}`;\n    log(msg);\n    status(msg);\n    ui.busy = false;\n    return msg;\n  }\n  let stream;\n  const constraints = {\n    audio: false,\n    video: { facingMode: ui.facing ? 'user' : 'environment', resizeMode: ui.crop ? 'crop-and-scale' : 'none' },\n  };\n  if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };\n  else constraints.video.height = { ideal: (window.innerHeight - document.getElementById('menubar').offsetHeight) };\n  try {\n    stream = await navigator.mediaDevices.getUserMedia(constraints);\n  } catch (err) {\n    if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';\n    else if (err.name === 'SourceUnavailableError') msg = 'camera not available';\n    else msg = `camera error: ${err.message || err}`;\n    output.innerText += `\\n${msg}`;\n    status(msg);\n    log('camera error:', err);\n    ui.busy = false;\n    return msg;\n  }\n  if (stream) video.srcObject = stream;\n  else {\n    ui.busy = false;\n    return 'camera stream empty';\n  }\n  const track = stream.getVideoTracks()[0];\n  const settings = track.getSettings();\n  // log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);\n  ui.camera = { name: track.label?.toLowerCase(), width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };\n  return new Promise((resolve) => {\n    video.onloadeddata = async () => {\n      video.width = video.videoWidth;\n      video.height = video.videoHeight;\n      canvas.width = video.width;\n      canvas.height = video.height;\n      canvas.style.width = canvas.width > canvas.height ? '100vw' : '';\n      canvas.style.height = canvas.width > canvas.height ? '' : '100vh';\n      ui.menuWidth.input.setAttribute('value', video.width);\n      ui.menuHeight.input.setAttribute('value', video.height);\n      // silly font resizing for paint-on-canvas since viewport can be zoomed\n      if (live) video.play();\n      // eslint-disable-next-line no-use-before-define\n      if (live && !ui.detectThread) runHumanDetect(video, canvas);\n      ui.busy = false;\n      // do once more because onresize events can be delayed or skipped\n      // if (video.width > window.innerWidth) await setupCamera();\n      status('');\n      resolve();\n    };\n  });\n}\n\nfunction initPerfMonitor() {\n  if (!bench) {\n    const gl = null;\n    // cosnt gl = human.tf.engine().backend.gpgpu.gl;\n    // if (!gl) log('bench cannot get tensorflow webgl context');\n    bench = new GLBench(gl, {\n      trackGPU: false, // this is really slow\n      chartHz: 20,\n      chartLen: 20,\n    });\n    bench.begin();\n  }\n}\n\n// wrapper for worker.postmessage that creates worker if one does not exist\nfunction webWorker(input, image, canvas, timestamp) {\n  if (!worker) {\n    // create new webworker and add event handler only once\n    log('creating worker thread');\n    worker = new Worker(ui.worker, { type: 'module' });\n    // after receiving message from webworker, parse&draw results and send new frame for processing\n    worker.addEventListener('message', (msg) => {\n      if (msg.data.result.performance && msg.data.result.performance.total) ui.detectFPS.push(1000 / msg.data.result.performance.total);\n      if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n      if (ui.bench) {\n        if (!bench) initPerfMonitor();\n        bench.nextFrame(timestamp);\n      }\n      if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n      lastDetectedResult = msg.data.result;\n      ui.framesDetect++;\n      if (!ui.drawThread) drawResults(input);\n      // eslint-disable-next-line no-use-before-define\n      ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n    });\n  }\n  // pass image data as arraybuffer to worker by reference to avoid copy\n  worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, userConfig }, [image.data.buffer]);\n}\n\n// main processing function when input is webcam, can use direct invocation or web worker\nfunction runHumanDetect(input, canvas, timestamp) {\n  // if live video\n  const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);\n  if (!live && input.srcObject) {\n    // stop ui refresh\n    if (ui.drawThread) cancelAnimationFrame(ui.drawThread);\n    if (ui.detectThread) cancelAnimationFrame(ui.detectThread);\n    ui.drawThread = null;\n    ui.detectThread = null;\n    // if we want to continue and camera not ready, retry in 0.5sec, else just give up\n    if (input.paused) log('camera paused');\n    else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);\n    else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);\n    clearTimeout(ui.drawThread);\n    ui.drawThread = null;\n    log('frame statistics: process:', ui.framesDetect, 'refresh:', ui.framesDraw);\n    log('memory', human.tf.engine().memory());\n    return;\n  }\n  status('');\n  if (ui.useWorker) {\n    // get image data from video as we cannot send html objects to webworker\n    const offscreen = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(canvas.width, canvas.height) : document.createElement('canvas');\n    offscreen.width = canvas.width;\n    offscreen.height = canvas.height;\n    const ctx = offscreen.getContext('2d');\n    ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n    const data = ctx.getImageData(0, 0, canvas.width, canvas.height);\n    // perform detection in worker\n    webWorker(input, data, canvas, userConfig, timestamp);\n  } else {\n    human.detect(input, userConfig).then((result) => {\n      if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);\n      if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n      if (ui.bench) {\n        if (!bench) initPerfMonitor();\n        bench.nextFrame(timestamp);\n      }\n      if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n      if (result.error) {\n        log(result.error);\n        document.getElementById('log').innerText += `\\nHuman error: ${result.error}`;\n      } else {\n        lastDetectedResult = result;\n        if (!ui.drawThread) drawResults(input);\n        ui.framesDetect++;\n        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n      }\n    });\n  }\n}\n\n// main processing function when input is image, can use direct invocation or web worker\nasync function processImage(input) {\n  return new Promise((resolve) => {\n    const image = new Image();\n    image.onload = async () => {\n      log('processing image:', encodeURI(image.src));\n      const canvas = document.getElementById('canvas');\n      image.width = image.naturalWidth;\n      image.height = image.naturalHeight;\n      canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;\n      canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;\n      const result = await human.detect(image, userConfig);\n      lastDetectedResult = result;\n      await drawResults(image);\n      const thumb = document.createElement('canvas');\n      thumb.className = 'thumbnail';\n      thumb.width = window.innerWidth / (ui.columns + 0.1);\n      thumb.height = thumb.width * canvas.height / canvas.width;\n      if (result.face && result.face.length > 0) {\n        thumb.title = result.face.map((a, i) => `#${i} face: ${Math.trunc(100 * a.faceConfidence)}% box: ${Math.trunc(100 * a.boxConfidence)}% age: ${Math.trunc(a.age)} gender: ${Math.trunc(100 * a.genderConfidence)}% ${a.gender}`).join(' | ');\n      } else {\n        thumb.title = 'no face detected';\n      }\n      const ctx = thumb.getContext('2d');\n      ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);\n      document.getElementById('samples-container').appendChild(thumb);\n      image.src = '';\n      resolve(true);\n    };\n    image.src = input;\n  });\n}\n\n// just initialize everything and call main function\nasync function detectVideo() {\n  document.getElementById('samples-container').style.display = 'none';\n  document.getElementById('canvas').style.display = 'block';\n  const video = document.getElementById('video');\n  const canvas = document.getElementById('canvas');\n  if ((video.srcObject !== null) && !video.paused) {\n    document.getElementById('play').style.display = 'block';\n    document.getElementById('btnStart').className = 'button button-start';\n    document.getElementById('btnStart').innerHTML = 'start<br>video';\n    status('paused');\n    video.pause();\n  } else {\n    const cameraError = await setupCamera();\n    if (!cameraError) {\n      document.getElementById('play').style.display = 'none';\n      for (const m of Object.values(menu)) m.hide();\n      status('');\n      document.getElementById('btnStart').className = 'button button-stop';\n      document.getElementById('btnStart').innerHTML = 'pause<br>video';\n      await video.play();\n      if (!ui.detectThread) runHumanDetect(video, canvas);\n    } else {\n      status(cameraError);\n    }\n  }\n}\n\n// just initialize everything and call main function\nasync function detectSampleImages() {\n  userConfig.videoOptimized = false; // force disable video optimizations\n  document.getElementById('play').style.display = 'none';\n  document.getElementById('canvas').style.display = 'none';\n  document.getElementById('samples-container').style.display = 'block';\n  log('running detection of sample images');\n  status('processing images');\n  document.getElementById('samples-container').innerHTML = '';\n  for (const m of Object.values(menu)) m.hide();\n  for (const image of ui.samples) await processImage(image);\n  status('');\n}\n\nfunction setupMenu() {\n  let x = [];\n  if (window.innerWidth > 800) {\n    // initial position of menu items, later it's calculated based on mouse coordinates\n    x = [`${document.getElementById('btnDisplay').offsetLeft - 50}px`, `${document.getElementById('btnImage').offsetLeft - 50}px`, `${document.getElementById('btnProcess').offsetLeft - 50}px`, `${document.getElementById('btnModel').offsetLeft - 50}px`];\n  } else {\n    // absolute minimum spacing for menus\n    x = ['0rem', '11rem', '21.1rem', '33rem'];\n  }\n\n  menu.display = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[0] });\n  menu.display.addBool('perf monitor', ui, 'bench', (val) => ui.bench = val);\n  menu.display.addBool('buffered output', ui, 'buffered', (val) => ui.buffered = val);\n  menu.display.addBool('crop & scale', ui, 'crop', (val) => {\n    ui.crop = val;\n    setupCamera();\n  });\n  menu.display.addBool('camera facing', ui, 'facing', (val) => {\n    ui.facing = val;\n    setupCamera();\n  });\n  menu.display.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.display.addBool('use 3D depth', human.draw.drawOptions, 'useDepth');\n  menu.display.addBool('draw with curves', human.draw.drawOptions, 'useCurves');\n  menu.display.addBool('print labels', human.draw.drawOptions, 'drawLabels');\n  menu.display.addBool('draw points', human.draw.drawOptions, 'drawPoints');\n  menu.display.addBool('draw boxes', human.draw.drawOptions, 'drawBoxes');\n  menu.display.addBool('draw polygons', human.draw.drawOptions, 'drawPolygons');\n  menu.display.addBool('fill polygons', human.draw.drawOptions, 'fillPolygons');\n\n  menu.image = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[1] });\n  menu.image.addBool('enabled', human.config.filter, 'enabled', (val) => human.config.filter.enabled = val);\n  ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));\n  ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));\n  menu.image.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));\n  menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));\n  menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));\n  menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));\n  menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));\n  menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));\n  menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));\n  menu.image.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.image.addBool('negative', human.config.filter, 'negative', (val) => human.config.filter.negative = val);\n  menu.image.addBool('sepia', human.config.filter, 'sepia', (val) => human.config.filter.sepia = val);\n  menu.image.addBool('vintage', human.config.filter, 'vintage', (val) => human.config.filter.vintage = val);\n  menu.image.addBool('kodachrome', human.config.filter, 'kodachrome', (val) => human.config.filter.kodachrome = val);\n  menu.image.addBool('technicolor', human.config.filter, 'technicolor', (val) => human.config.filter.technicolor = val);\n  menu.image.addBool('polaroid', human.config.filter, 'polaroid', (val) => human.config.filter.polaroid = val);\n\n  menu.process = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[2] });\n  menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);\n  menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);\n  // menu.process.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);\n  // menu.process.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);\n  menu.process.addBool('use web worker', ui, 'useWorker');\n  menu.process.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.process.addLabel('model parameters');\n  menu.process.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {\n    human.config.face.detector.maxFaces = parseInt(val);\n    human.config.body.maxDetections = parseInt(val);\n    human.config.hand.maxHands = parseInt(val);\n  });\n  menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {\n    human.config.face.detector.skipFrames = parseInt(val);\n    human.config.face.emotion.skipFrames = parseInt(val);\n    human.config.face.age.skipFrames = parseInt(val);\n    human.config.hand.skipFrames = parseInt(val);\n  });\n  menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {\n    human.config.face.detector.minConfidence = parseFloat(val);\n    human.config.face.gender.minConfidence = parseFloat(val);\n    human.config.face.emotion.minConfidence = parseFloat(val);\n    human.config.hand.minConfidence = parseFloat(val);\n  });\n  menu.process.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {\n    human.config.face.detector.scoreThreshold = parseFloat(val);\n    human.config.hand.scoreThreshold = parseFloat(val);\n    human.config.body.scoreThreshold = parseFloat(val);\n  });\n  menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {\n    human.config.face.detector.iouThreshold = parseFloat(val);\n    human.config.hand.iouThreshold = parseFloat(val);\n  });\n  menu.process.addBool('detection rotation', human.config.face.detector, 'rotation', (val) => {\n    human.config.face.detector.rotation = val;\n    human.config.hand.rotation = val;\n  });\n  menu.process.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.process.addButton('process sample images', 'process images', () => detectSampleImages());\n  menu.process.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.process.addChart('FPS', 'FPS');\n\n  menu.models = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[3] });\n  menu.models.addBool('face detect', human.config.face, 'enabled', (val) => human.config.face.enabled = val);\n  menu.models.addBool('face mesh', human.config.face.mesh, 'enabled', (val) => human.config.face.mesh.enabled = val);\n  menu.models.addBool('face iris', human.config.face.iris, 'enabled', (val) => human.config.face.iris.enabled = val);\n  menu.models.addBool('face description', human.config.face.description, 'enabled', (val) => human.config.face.age.description = val);\n  // menu.models.addBool('face age', human.config.face.age, 'enabled', (val) => human.config.face.age.enabled = val);\n  // menu.models.addBool('face gender', human.config.face.gender, 'enabled', (val) => human.config.face.gender.enabled = val);\n  menu.models.addBool('face emotion', human.config.face.emotion, 'enabled', (val) => human.config.face.emotion.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('body pose', human.config.body, 'enabled', (val) => human.config.body.enabled = val);\n  menu.models.addBool('hand pose', human.config.hand, 'enabled', (val) => human.config.hand.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('gestures', human.config.gesture, 'enabled', (val) => human.config.gesture.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('object detection', human.config.object, 'enabled', (val) => human.config.object.enabled = val);\n  menu.models.addHTML('<hr style=\"border-style: inset; border-color: dimgray\">');\n  menu.models.addBool('face compare', compare, 'enabled', (val) => {\n    compare.enabled = val;\n    compare.original = null;\n  });\n\n  document.getElementById('btnDisplay').addEventListener('click', (evt) => menu.display.toggle(evt));\n  document.getElementById('btnImage').addEventListener('click', (evt) => menu.image.toggle(evt));\n  document.getElementById('btnProcess').addEventListener('click', (evt) => menu.process.toggle(evt));\n  document.getElementById('btnModel').addEventListener('click', (evt) => menu.models.toggle(evt));\n  document.getElementById('btnStart').addEventListener('click', () => detectVideo());\n  document.getElementById('play').addEventListener('click', () => detectVideo());\n}\n\nasync function drawWarmup(res) {\n  const canvas = document.getElementById('canvas');\n  canvas.width = res.canvas.width;\n  canvas.height = res.canvas.height;\n  const ctx = canvas.getContext('2d');\n  ctx.drawImage(res.canvas, 0, 0, res.canvas.width, res.canvas.height, 0, 0, canvas.width, canvas.height);\n  await human.draw.all(canvas, res);\n}\n\nasync function main() {\n  log('demo starting ...');\n  setupMenu();\n  document.getElementById('log').innerText = `Human: version ${human.version}`;\n  if (ui.modelsPreload && !ui.useWorker) {\n    status('loading');\n    await human.load(userConfig); // this is not required, just pre-loads all models\n    const loaded = Object.keys(human.models).filter((a) => human.models[a]);\n    log('demo loaded models:', loaded);\n  }\n  if (!ui.useWorker) {\n    status('initializing');\n    const res = await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference\n    if (res && res.canvas && ui.drawWarmup) await drawWarmup(res);\n  }\n  status('human: ready');\n  document.getElementById('loader').style.display = 'none';\n  document.getElementById('play').style.display = 'block';\n  log('demo ready...');\n}\n\nwindow.onload = main;\nwindow.onresize = setupCamera;\n"],
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diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index 0d730af5..e8f77967 100644
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
@@ -5,14 +5,14 @@
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O0=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],I0=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Z0=[33,133,362,263,1,78,308],hr=O0.map(t=>m1[t]),ir=I0.map(t=>m1[t]),ar=Z0.map(t=>m1[t]);var p1=C.leftEyeLower0,u1=C.rightEyeLower0,_e={leftBounds:[p1[0],p1[p1.length-1]],rightBounds:[u1[0],u1[u1.length-1]]},We={count:468,mouth:13,symmetryLine:[13,C.midwayBetweenEyes[0]]},bt={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},oe={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ne(t,e,n,c){for(let r=0;r<f1.length;r++){let{key:A,indices:_}=f1[r],s=C[`${n}${A}`];if(!c||c.includes(A))for(let h=0;h<_.length;h++){let a=_[h];t[s[h]]=[e[a][0],e[a][1],(e[a][2]+t[s[h]][2])/2]}}}var b1=class{constructor(e,n,c){var r,A;this.storedBoxes=[],this.boundingBoxDetector=e,this.meshDetector=n,this.irisModel=c,this.boxSize=((r=e==null?void 0:e.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((A=e==null?void 0:e.model)==null?void 0:A.inputs[0].shape[2]),this.irisSize=(c==null?void 0:c.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(e,n,c,r){let A=xe({startPoint:n.startPoint,endPoint:n.endPoint}),_=e.map(i=>[A[0]/this.meshSize*(i[0]-this.meshSize/2),A[1]/this.meshSize*(i[1]-this.meshSize/2),i[2]]),s=c!==0?je(c,[0,0]):Se,h=c!==0?_.map(i=>[...ut(i,s),i[2]]):_,a=c!==0?pt(r):Se,x=[...ce({startPoint:n.startPoint,endPoint:n.endPoint}),1];return h.map(i=>[i[0]+D(x,a[0]),i[1]+D(x,a[1]),i[2]])}getLeftToRightEyeDepthDifference(e){let n=e[_e.leftBounds[0]][2],c=e[_e.rightBounds[0]][2];return n-c}getEyeBox(e,n,c,r,A=!1){let _=Ee(Re(this.calculateLandmarksBoundingBox([e[c],e[r]]),this.irisEnlarge)),s=xe(_),h=o.image.cropAndResize(n,[[_.startPoint[1]/this.meshSize,_.startPoint[0]/this.meshSize,_.endPoint[1]/this.meshSize,_.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return A&&o.ENV.flags.IS_BROWSER&&(h=o.image.flipLeftRight(h)),{box:_,boxSize:s,crop:h}}getEyeCoords(e,n,c,r=!1){let A=[];for(let _=0;_<oe.numCoordinates;_++){let s=e[_*3],h=e[_*3+1],a=e[_*3+2];A.push([(r?1-s/this.irisSize:s/this.irisSize)*c[0]+n.startPoint[0],h/this.irisSize*c[1]+n.startPoint[1],a])}return{rawCoords:A,iris:A.slice(oe.index)}}getAdjustedIrisCoords(e,n,c){let r=e[C[`${c}EyeUpper0`][oe.upperCenter]][2],A=e[C[`${c}EyeLower0`][oe.lowerCenter]][2],_=(r+A)/2;return n.map((s,h)=>{let a=_;return h===2?a=r:h===4&&(a=A),[s[0],s[1],a]})}async predict(e,n){let c=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(e),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let _ of r.boxes)this.storedBoxes.push({startPoint:_.box.startPoint.dataSync(),endPoint:_.box.endPoint.dataSync(),landmarks:_.landmarks,confidence:_.confidence});this.storedBoxes.length>0&&(c=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),c){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let _=0;_<this.storedBoxes.length;_++){let s=dt({startPoint:this.storedBoxes[_].startPoint,endPoint:this.storedBoxes[_].endPoint},r.scaleFactor),h=Re(s),a=Ee(h),x=this.storedBoxes[_].landmarks.arraySync(),i=this.storedBoxes[_].confidence;this.storedBoxes[_]={...a,confidence:i,landmarks:x}}}r&&r.boxes&&r.boxes.forEach(_=>{_.box.startPoint.dispose(),_.box.endPoint.dispose(),_.landmarks.dispose()});let A=o.tidy(()=>this.storedBoxes.map((_,s)=>{let h=_.confidence,a,x=0,i;if(n.face.detector.rotation&&n.face.mesh.enabled&&o.ENV.flags.IS_BROWSER){let[b,f]=_.landmarks.length>=We.count?We.symmetryLine:bt.symmetryLine;x=d1(_.landmarks[b],_.landmarks[f]);let u=ce({startPoint:_.startPoint,endPoint:_.endPoint}),P=[u[0]/e.shape[2],u[1]/e.shape[1]],M=o.image.rotateWithOffset(e,x,0,P);i=je(-x,u),n.face.mesh.enabled?a=Ae({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.meshSize,this.meshSize]).div(255):a=Ae({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.boxSize,this.boxSize]).div(255)}else{i=Se;let b=e.clone();n.face.mesh.enabled?a=Ae({startPoint:_.startPoint,endPoint:_.endPoint},b,[this.meshSize,this.meshSize]).div(255):a=Ae({startPoint:_.startPoint,endPoint:_.endPoint},b,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,boxConfidence:h,confidence:_.confidence,image:a};let[,l,m]=this.meshDetector.predict(a),p=l.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let z=o.reshape(m,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:b,boxSize:f,crop:u}=this.getEyeBox(z,a,_e.leftBounds[0],_e.leftBounds[1],!0),{box:P,boxSize:M,crop:I}=this.getEyeBox(z,a,_e.rightBounds[0],_e.rightBounds[1]),J=this.irisModel.predict(o.concat([u,I])).dataSync(),s0=J.slice(0,oe.numCoordinates*3),{rawCoords:ct,iris:h0}=this.getEyeCoords(s0,b,f,!0),i0=J.slice(oe.numCoordinates*3),{rawCoords:At,iris:a0}=this.getEyeCoords(i0,P,M),_t=this.getLeftToRightEyeDepthDifference(z);Math.abs(_t)<30?(Ne(z,ct,"left",null),Ne(z,At,"right",null)):_t<1?Ne(z,ct,"left",["EyeUpper0","EyeLower0"]):Ne(z,At,"right",["EyeUpper0","EyeLower0"]);let x0=this.getAdjustedIrisCoords(z,h0,"left"),y0=this.getAdjustedIrisCoords(z,a0,"right");z=z.concat(x0).concat(y0)}let T=this.transformRawCoords(z,_,x,i);_=Re(this.calculateLandmarksBoundingBox(T),1.5);let S=o.tensor2d(T);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&o.ENV.flags.IS_BROWSER){let[b,f]=_.landmarks.length>=We.count?We.symmetryLine:bt.symmetryLine;x=d1(_.landmarks[b],_.landmarks[f]);let u=ce({startPoint:_.startPoint,endPoint:_.endPoint}),P=[u[0]/e.shape[2],u[1]/e.shape[1]],M=o.image.rotateWithOffset(e,x,0,P);i=je(-x,u),a=Ae({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.meshSize,this.meshSize]).div(255)}let R={coords:S,box:_,faceConfidence:p,boxConfidence:h,image:a,rawCoords:z},k=Ee(_);return this.storedBoxes[s]={...k,landmarks:T,confidence:_.confidence,faceConfidence:p},R}));return A=A.filter(_=>_!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(_=>_.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=A.length,A}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),c=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A,landmarks:e}}};var T1=class{constructor(e,n,c,r){this.facePipeline=new b1(e,n,c),this.config=r}async estimateFaces(e,n){let c=await this.facePipeline.predict(e,n),r=[];for(let A of c||[]){if(A.isDisposedInternal)continue;let _=A.coords?A.coords.arraySync():[],s=_.map(i=>[i[0]/e.shape[2],i[1]/e.shape[1],i[2]/this.facePipeline.meshSize]),h={};if(_&&_.length>0)for(let i of Object.keys(C))h[i]=C[i].map(l=>_[l]);let a=A.box?[Math.max(0,A.box.startPoint[0]),Math.max(0,A.box.startPoint[1]),Math.min(e.shape[1],A.box.endPoint[0])-Math.max(0,A.box.startPoint[0]),Math.min(e.shape[2],A.box.endPoint[1])-Math.max(0,A.box.startPoint[1])]:0,x=A.box?[A.box.startPoint[0]/e.shape[2],A.box.startPoint[1]/e.shape[1],(A.box.endPoint[0]-A.box.startPoint[0])/e.shape[2],(A.box.endPoint[1]-A.box.startPoint[1])/e.shape[1]]:[];r.push({confidence:A.faceConfidence||A.boxConfidence||0,boxConfidence:A.boxConfidence,faceConfidence:A.faceConfidence,box:a,boxRaw:x,mesh:_,meshRaw:s,annotations:h,image:A.image?A.image.clone():null}),A.coords&&A.coords.dispose(),A.image&&A.image.dispose()}return r}},te=[null,null,null];async function P1(t){te=await Promise.all([!te[0]&&t.face.enabled?lt(t):null,!te[1]&&t.face.mesh.enabled?o.loadGraphModel(t.face.mesh.modelPath,{fromTFHub:t.face.mesh.modelPath.includes("tfhub.dev")}):null,!te[2]&&t.face.iris.enabled?o.loadGraphModel(t.face.iris.modelPath,{fromTFHub:t.face.iris.modelPath.includes("tfhub.dev")}):null]);let e=new T1(te[0],te[1],te[2],t);return t.face.mesh.enabled&&t.debug&&g(`load model: ${t.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),t.face.iris.enabled&&t.debug&&g(`load model: ${t.face.iris.modelPath.match(/\/(.*)\./)[1]}`),e}var L0=ee;var k1={};X(k1,{PoseNet:()=>O1,load:()=>I1});function V0(t){let[e,n,c,r]=t;return{offsets:e,heatmap:n,displacementFwd:c,displacementBwd:r}}var v1=class{constructor(e){this.model=e}predict(e){return o.tidy(()=>{let c=e.toFloat().div(127.5).sub(1).expandDims(0),A=this.model.predict(c).map(s=>s.squeeze([0])),_=V0(A);return{heatmapScores:_.heatmap.sigmoid(),offsets:_.offsets,displacementFwd:_.displacementFwd,displacementBwd:_.displacementBwd}})}dispose(){this.model.dispose()}};function z1(t){return Math.floor(t/2)}var M1=class{constructor(e,n){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=n}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(e){for(;e>0&&this.less(z1(e),e);)this.exchange(e,z1(e)),e=z1(e)}sink(e){for(;2*e<=this.numberOfElements;){let n=2*e;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(e,n))break;this.exchange(e,n),e=n}}getValueAt(e){return this.getElementValue(this.priorityQueue[e])}less(e,n){return this.getValueAt(e)<this.getValueAt(n)}exchange(e,n){let c=this.priorityQueue[e];this.priorityQueue[e]=this.priorityQueue[n],this.priorityQueue[n]=c}};function H0(t,e,n,c,r,A){let[_,s]=A.shape,h=!0,a=Math.max(n-r,0),x=Math.min(n+r+1,_);for(let i=a;i<x;++i){let l=Math.max(c-r,0),m=Math.min(c+r+1,s);for(let p=l;p<m;++p)if(A.get(i,p,t)>e){h=!1;break}if(!h)break}return h}function gt(t,e,n){let[c,r,A]=n.shape,_=new M1(c*r*A,({score:s})=>s);for(let s=0;s<c;++s)for(let h=0;h<r;++h)for(let a=0;a<A;++a){let x=n.get(s,h,a);x<t||H0(a,x,s,h,e,n)&&_.enqueue({score:x,part:{heatmapY:s,heatmapX:h,id:a}})}return _}var se=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],he=se.length,ye=se.reduce((t,e,n)=>(t[e]=n,t),{}),X0=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Rr=X0.map(([t,e])=>[ye[t],ye[e]]),Tt=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function R1(t,e,n,c){return{y:c.get(t,e,n),x:c.get(t,e,n+he)}}function ke(t,e,n){let{heatmapY:c,heatmapX:r,id:A}=t,{y:_,x:s}=R1(c,r,A,n);return{x:t.heatmapX*e+s,y:t.heatmapY*e+_}}function E1(t,e,n){return t<e?e:t>n?n:t}function Pt(t,e,n,c){let r=n-t,A=c-e;return r*r+A*A}function S1(t,e){return{x:t.x+e.x,y:t.y+e.y}}function vt(t,e){let n=e.shape[0],c=new Float32Array(n);for(let r=0;r<n;r++){let A=e.get(r,0),_=e.get(r,1);c[r]=t.get(A,_,r)}return c}function F0(t,e,n,c){return{y:c.get(t,e,n),x:c.get(t,e,n+he)}}function q0(t,e){let n=[];for(let c=0;c<he;c++){let r=t.get(c,0).valueOf(),A=t.get(c,1).valueOf(),{x:_,y:s}=F0(r,A,c,e);n.push(s),n.push(_)}return o.tensor2d(n,[he,2])}function zt(t,e,n){return o.tidy(()=>t.toTensor().mul(o.scalar(e,"int32")).toFloat().add(q0(t,n)))}function B0(t,e){return o.tidy(()=>{let n=t.div(o.scalar(e,"int32"));return t.sub(n.mul(o.scalar(e,"int32")))})}function Mt(t){let[e,n,c]=t.shape;return o.tidy(()=>{let A=t.reshape([e*n,c]).argMax(0),_=A.div(o.scalar(n,"int32")).expandDims(1),s=B0(A,n).expandDims(1);return o.concat([_,s],1)})}var Rt=Tt.map(([t,e])=>[ye[t],ye[e]]),j1=Rt.map(([,t])=>t),Et=Rt.map(([t])=>t),C0=16;function Y0(t,e,n){let c=n.shape[2]/2;return{y:n.get(e.y,e.x,t),x:n.get(e.y,e.x,c+t)}}function W1(t,e,n,c){return{y:E1(Math.round(t.y/e),0,n-1),x:E1(Math.round(t.x/e),0,c-1)}}function St(t,e,n,c,r,A,_,s=2){let[h,a]=c.shape,x=W1(e.position,A,h,a),i=Y0(t,x,_),m=S1(e.position,i);for(let z=0;z<s;z++){let T=W1(m,A,h,a),S=R1(T.y,T.x,n,r);m=S1({x:T.x*A,y:T.y*A},{x:S.x,y:S.y})}let p=W1(m,A,h,a),y=c.get(p.y,p.x,n);return{position:m,part:se[n],score:y}}function jt(t,e,n,c,r,A){let _=e.shape[2],s=j1.length,h=new Array(_),{part:a,score:x}=t,i=ke(a,c,n);h[a.id]={score:x,part:se[a.id],position:i};for(let l=s-1;l>=0;--l){let m=j1[l],p=Et[l];h[m]&&!h[p]&&(h[p]=St(l,h[m],p,e,n,c,A))}for(let l=0;l<s;++l){let m=Et[l],p=j1[l];h[m]&&!h[p]&&(h[p]=St(l,h[m],p,e,n,c,r))}return h}async function Wt(t,e,n){let c=0,r=Mt(t),A=await Promise.all([t.buffer(),e.buffer(),r.buffer()]),_=A[0],s=A[1],h=A[2],a=zt(h,C0,s),x=await a.buffer(),l=Array.from(vt(_,h)).map((p,y)=>(c+=p,{position:{y:x.get(y,0),x:x.get(y,1)},part:se[y],score:p})),m=l.filter(p=>p.score>n);return r.dispose(),a.dispose(),{keypoints:m,score:c/l.length}}var J0=1,Nt=16;function kt(t,e,{x:n,y:c},r){return t.some(({keypoints:A})=>{let _=A[r].position;return Pt(c,n,_.y,_.x)<=e})}function K0(t,e,n){return n.reduce((r,{position:A,score:_},s)=>(kt(t,e,A,s)||(r+=_),r),0)/n.length}function Ot(t,e,n,c,r,A,_){let s=[],h=gt(_,J0,t),a=r^2;for(;s.length<A&&!h.empty();){let x=h.dequeue(),i=ke(x.part,Nt,e);if(kt(s,a,i,x.part.id))continue;let l=jt(x,t,e,Nt,n,c),m=K0(s,a,l);m>_&&s.push({keypoints:l,score:m})}return s}async function It(t){return Promise.all(t.map(e=>e.buffer()))}function U0(t,e,n){return{score:t.score,keypoints:t.keypoints.map(({score:c,part:r,position:A})=>({score:c,part:r,position:{x:Math.trunc(A.x*n),y:Math.trunc(A.y*e)}}))}}function Zt(t,[e,n]){let c=t.squeeze(0),r=c.resizeBilinear([e,n]);return c.dispose(),r}function N1(t,[e,n],[c,r]){return t.map(_=>U0(_,e/c,n/r))}async function D0(t,e,n,c){return new Promise(async r=>{let A=await It([e.heatmapScores,e.offsets,e.displacementFwd,e.displacementBwd]),_=A[0],s=A[1],h=A[2],a=A[3],x=await Ot(_,s,h,a,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),i=N1(x,[t.shape[1],t.shape[2]],[c,c]);r(i)})}async function G0(t,e,n,c){return new Promise(async r=>{let A=await Wt(e.heatmapScores,e.offsets,n.body.scoreThreshold),_=N1([A],[t.shape[1],t.shape[2]],[c,c]);r(_)})}var O1=class{constructor(e){this.baseModel=e,this.inputSize=e.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(e,n){let c=Zt(e,[this.inputSize,this.inputSize]),r=this.baseModel.predict(c,n),A=n.body.maxDetections<2?await G0(e,r,n,this.inputSize):await D0(e,r,n,this.inputSize);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),c.dispose(),A}dispose(){this.baseModel.dispose()}};async function I1(t){let e=await o.loadGraphModel(t.body.modelPath),n=new v1(e);return t.debug&&g(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`),new O1(n)}var X1={};X(X1,{HandPose:()=>q1,load:()=>B1});function Oe(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function we(t){return[t.startPoint[0]+(t.endPoint[0]-t.startPoint[0])/2,t.startPoint[1]+(t.endPoint[1]-t.startPoint[1])/2]}function Lt(t,e,n){let c=e.shape[1],r=e.shape[2],A=[[t.startPoint[1]/c,t.startPoint[0]/r,t.endPoint[1]/c,t.endPoint[0]/r]];return o.image.cropAndResize(e,A,[0],n)}function Vt(t,e){let n=[t.startPoint[0]*e[0],t.startPoint[1]*e[1]],c=[t.endPoint[0]*e[0],t.endPoint[1]*e[1]],r=t.palmLandmarks.map(A=>[A[0]*e[0],A[1]*e[1]]);return{startPoint:n,endPoint:c,palmLandmarks:r,confidence:t.confidence}}function Ie(t,e=1.5){let n=we(t),c=Oe(t),r=[e*c[0]/2,e*c[1]/2],A=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:A,endPoint:_,palmLandmarks:t.palmLandmarks}}function Ze(t){let e=we(t),n=Oe(t),r=Math.max(...n)/2,A=[e[0]-r,e[1]-r],_=[e[0]+r,e[1]+r];return{startPoint:A,endPoint:_,palmLandmarks:t.palmLandmarks}}var Z1=class{constructor(e,n,c){this.model=e,this.anchors=c.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=o.tensor2d(this.anchors),this.inputSize=n,this.inputSizeTensor=o.tensor1d([n,n]),this.doubleInputSizeTensor=o.tensor1d([n*2,n*2])}normalizeBoxes(e){return o.tidy(()=>{let 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n=e.map(_=>_[0]),c=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A}}};var 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Jt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],Kt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var V;async function Y1(t){return V||(V=await o.loadGraphModel(t.body.modelPath),V.width=parseInt(V.signature.inputs["input_1:0"].tensorShape.dim[2].size),V.height=parseInt(V.signature.inputs["input_1:0"].tensorShape.dim[1].size),t.debug&&g(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`)),V}async function J1(t,e){if(!V||!e.body.enabled)return null;let n={width:t.shape[2],height:t.shape[1]},c=o.image.resizeBilinear(t,[V.width,V.height],!1),r=o.div(c,[255]);c.dispose();let A;if(e.profile){let a=await o.profile(()=>V.predict(r));A=a.result.find(x=>x.size===195||x.size===155).dataSync(),a.result.forEach(x=>x.dispose()),O("blazepose",a)}else{let a=await V.predict(r);A=a.find(x=>x.size===195||x.size===155).dataSync(),a.forEach(x=>x.dispose())}r.dispose();let _=[],s=A.length===195?Jt:Kt,h=5;for(let 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k0=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],O0=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],I0=[33,133,362,263,1,78,308],sr=k0.map(t=>g1[t]),hr=O0.map(t=>g1[t]),ir=I0.map(t=>g1[t]);var T1=Y.leftEyeLower0,P1=Y.rightEyeLower0,oe={leftBounds:[T1[0],T1[T1.length-1]],rightBounds:[P1[0],P1[P1.length-1]]},Oe={count:468,mouth:13,symmetryLine:[13,Y.midwayBetweenEyes[0]]},Pt={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},se={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ie(t,e,n,c){for(let r=0;r<b1.length;r++){let{key:A,indices:_}=b1[r],s=Y[`${n}${A}`];if(!c||c.includes(A))for(let h=0;h<_.length;h++){let i=_[h];t[s[h]]=[e[i][0],e[i][1],(e[i][2]+t[s[h]][2])/2]}}}var v1=class{constructor(e,n,c){var r,A;this.storedBoxes=[],this.boundingBoxDetector=e,this.meshDetector=n,this.irisModel=c,this.boxSize=((r=e==null?void 0:e.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((A=e==null?void 0:e.model)==null?void 0:A.inputs[0].shape[2]),this.irisSize=(c==null?void 0:c.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(e,n,c,r){let A=ye({startPoint:n.startPoint,endPoint:n.endPoint}),_=e.map(a=>[A[0]/this.meshSize*(a[0]-this.meshSize/2),A[1]/this.meshSize*(a[1]-this.meshSize/2),a[2]]),s=c!==0?ke(c,[0,0]):Ne,h=c!==0?_.map(a=>[...Tt(a,s),a[2]]):_,i=c!==0?gt(r):Ne,y=[...Ae({startPoint:n.startPoint,endPoint:n.endPoint}),1];return h.map(a=>[a[0]+D(y,i[0]),a[1]+D(y,i[1]),a[2]])}getLeftToRightEyeDepthDifference(e){let n=e[oe.leftBounds[0]][2],c=e[oe.rightBounds[0]][2];return n-c}getEyeBox(e,n,c,r,A=!1){let _=We(je(this.calculateLandmarksBoundingBox([e[c],e[r]]),this.irisEnlarge)),s=ye(_),h=o.image.cropAndResize(n,[[_.startPoint[1]/this.meshSize,_.startPoint[0]/this.meshSize,_.endPoint[1]/this.meshSize,_.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return A&&o.ENV.flags.IS_BROWSER&&(h=o.image.flipLeftRight(h)),{box:_,boxSize:s,crop:h}}getEyeCoords(e,n,c,r=!1){let A=[];for(let _=0;_<se.numCoordinates;_++){let s=e[_*3],h=e[_*3+1],i=e[_*3+2];A.push([(r?1-s/this.irisSize:s/this.irisSize)*c[0]+n.startPoint[0],h/this.irisSize*c[1]+n.startPoint[1],i])}return{rawCoords:A,iris:A.slice(se.index)}}getAdjustedIrisCoords(e,n,c){let r=e[Y[`${c}EyeUpper0`][se.upperCenter]][2],A=e[Y[`${c}EyeLower0`][se.lowerCenter]][2],_=(r+A)/2;return n.map((s,h)=>{let i=_;return h===2?i=r:h===4&&(i=A),[s[0],s[1],i]})}async predict(e,n){let c=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(e),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let _ of r.boxes)this.storedBoxes.push({startPoint:_.box.startPoint.dataSync(),endPoint:_.box.endPoint.dataSync(),landmarks:_.landmarks,confidence:_.confidence});this.storedBoxes.length>0&&(c=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),c){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let _=0;_<this.storedBoxes.length;_++){let s=pt({startPoint:this.storedBoxes[_].startPoint,endPoint:this.storedBoxes[_].endPoint},r.scaleFactor),h=je(s),i=We(h),y=this.storedBoxes[_].landmarks.arraySync(),a=this.storedBoxes[_].confidence;this.storedBoxes[_]={...i,confidence:a,landmarks:y}}}r&&r.boxes&&r.boxes.forEach(_=>{_.box.startPoint.dispose(),_.box.endPoint.dispose(),_.landmarks.dispose()});let A=o.tidy(()=>this.storedBoxes.map((_,s)=>{let h=_.confidence,i,y=0,a;if(n.face.detector.rotation&&n.face.mesh.enabled&&o.ENV.flags.IS_BROWSER){let[u,f]=_.landmarks.length>=Oe.count?Oe.symmetryLine:Pt.symmetryLine;y=u1(_.landmarks[u],_.landmarks[f]);let b=Ae({startPoint:_.startPoint,endPoint:_.endPoint}),v=[b[0]/e.shape[2],b[1]/e.shape[1]],R=o.image.rotateWithOffset(e,y,0,v);a=ke(-y,b),n.face.mesh.enabled?i=_e({startPoint:_.startPoint,endPoint:_.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=_e({startPoint:_.startPoint,endPoint:_.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{a=Ne;let u=e.clone();n.face.mesh.enabled?i=_e({startPoint:_.startPoint,endPoint:_.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=_e({startPoint:_.startPoint,endPoint:_.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,boxConfidence:h,confidence:_.confidence,image:i};let[,l,p]=this.meshDetector.predict(i),m=l.dataSync()[0];if(m<n.face.detector.minConfidence)return null;let P=o.reshape(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:f,crop:b}=this.getEyeBox(P,i,oe.leftBounds[0],oe.leftBounds[1],!0),{box:v,boxSize:R,crop:O}=this.getEyeBox(P,i,oe.rightBounds[0],oe.rightBounds[1]),q=this.irisModel.predict(o.concat([b,O])).dataSync(),t1=q.slice(0,se.numCoordinates*3),{rawCoords:pe,iris:$}=this.getEyeCoords(t1,u,f,!0),h0=q.slice(se.numCoordinates*3),{rawCoords:st,iris:i0}=this.getEyeCoords(h0,v,R),ht=this.getLeftToRightEyeDepthDifference(P);Math.abs(ht)<30?(Ie(P,pe,"left",null),Ie(P,st,"right",null)):ht<1?Ie(P,pe,"left",["EyeUpper0","EyeLower0"]):Ie(P,st,"right",["EyeUpper0","EyeLower0"]);let a0=this.getAdjustedIrisCoords(P,$,"left"),x0=this.getAdjustedIrisCoords(P,i0,"right");P=P.concat(a0).concat(x0)}let T=this.transformRawCoords(P,_,y,a);_=je(this.calculateLandmarksBoundingBox(T),1.5);let M=o.tensor2d(T);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&o.ENV.flags.IS_BROWSER){let[u,f]=_.landmarks.length>=Oe.count?Oe.symmetryLine:Pt.symmetryLine;y=u1(_.landmarks[u],_.landmarks[f]);let b=Ae({startPoint:_.startPoint,endPoint:_.endPoint}),v=[b[0]/e.shape[2],b[1]/e.shape[1]],R=o.image.rotateWithOffset(e,y,0,v);a=ke(-y,b),i=_e({startPoint:_.startPoint,endPoint:_.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let S={coords:M,box:_,faceConfidence:m,boxConfidence:h,image:i,rawCoords:P},Z=We(_);return this.storedBoxes[s]={...Z,landmarks:T,confidence:_.confidence,faceConfidence:m},S}));return A=A.filter(_=>_!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(_=>_.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=A.length,A}calculateLandmarksBoundingBox(e){let n=e.map(_=>_[0]),c=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A,landmarks:e}}};var M1=class{constructor(e,n,c,r){this.facePipeline=new v1(e,n,c),this.config=r}async estimateFaces(e,n){let c=await this.facePipeline.predict(e,n),r=[];for(let A of c||[]){if(A.isDisposedInternal)continue;let _=A.coords?A.coords.arraySync():[],s=_.map(a=>[a[0]/e.shape[2],a[1]/e.shape[1],a[2]/this.facePipeline.meshSize]),h={};if(_&&_.length>0)for(let a of Object.keys(Y))h[a]=Y[a].map(l=>_[l]);let i=A.box?[Math.max(0,A.box.startPoint[0]),Math.max(0,A.box.startPoint[1]),Math.min(e.shape[1],A.box.endPoint[0])-Math.max(0,A.box.startPoint[0]),Math.min(e.shape[2],A.box.endPoint[1])-Math.max(0,A.box.startPoint[1])]:0,y=A.box?[A.box.startPoint[0]/e.shape[2],A.box.startPoint[1]/e.shape[1],(A.box.endPoint[0]-A.box.startPoint[0])/e.shape[2],(A.box.endPoint[1]-A.box.startPoint[1])/e.shape[1]]:[];r.push({confidence:A.faceConfidence||A.boxConfidence||0,boxConfidence:A.boxConfidence,faceConfidence:A.faceConfidence,box:i,boxRaw:y,mesh:_,meshRaw:s,annotations:h,image:A.image?A.image.clone():null}),A.coords&&A.coords.dispose(),A.image&&A.image.dispose()}return r}},ne=[null,null,null];async function R1(t){ne=await Promise.all([!ne[0]&&t.face.enabled?mt(t):null,!ne[1]&&t.face.mesh.enabled?o.loadGraphModel(t.face.mesh.modelPath,{fromTFHub:t.face.mesh.modelPath.includes("tfhub.dev")}):null,!ne[2]&&t.face.iris.enabled?o.loadGraphModel(t.face.iris.modelPath,{fromTFHub:t.face.iris.modelPath.includes("tfhub.dev")}):null]);let e=new M1(ne[0],ne[1],ne[2],t);return t.face.mesh.enabled&&t.debug&&g(`load model: ${t.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),t.face.iris.enabled&&t.debug&&g(`load model: ${t.face.iris.modelPath.match(/\/(.*)\./)[1]}`),e}var Z0=te;var L1={};X(L1,{PoseNet:()=>V1,load:()=>H1});function L0(t){let[e,n,c,r]=t;return{offsets:e,heatmap:n,displacementFwd:c,displacementBwd:r}}var E1=class{constructor(e){this.model=e}predict(e){return o.tidy(()=>{let c=e.toFloat().div(127.5).sub(1).expandDims(0),A=this.model.predict(c).map(s=>s.squeeze([0])),_=L0(A);return{heatmapScores:_.heatmap.sigmoid(),offsets:_.offsets,displacementFwd:_.displacementFwd,displacementBwd:_.displacementBwd}})}dispose(){this.model.dispose()}};function S1(t){return Math.floor(t/2)}var j1=class{constructor(e,n){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=n}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(e){for(;e>0&&this.less(S1(e),e);)this.exchange(e,S1(e)),e=S1(e)}sink(e){for(;2*e<=this.numberOfElements;){let n=2*e;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(e,n))break;this.exchange(e,n),e=n}}getValueAt(e){return this.getElementValue(this.priorityQueue[e])}less(e,n){return this.getValueAt(e)<this.getValueAt(n)}exchange(e,n){let c=this.priorityQueue[e];this.priorityQueue[e]=this.priorityQueue[n],this.priorityQueue[n]=c}};function V0(t,e,n,c,r,A){let[_,s]=A.shape,h=!0,i=Math.max(n-r,0),y=Math.min(n+r+1,_);for(let a=i;a<y;++a){let l=Math.max(c-r,0),p=Math.min(c+r+1,s);for(let m=l;m<p;++m)if(A.get(a,m,t)>e){h=!1;break}if(!h)break}return h}function vt(t,e,n){let[c,r,A]=n.shape,_=new j1(c*r*A,({score:s})=>s);for(let s=0;s<c;++s)for(let h=0;h<r;++h)for(let i=0;i<A;++i){let y=n.get(s,h,i);y<t||V0(i,y,s,h,e,n)&&_.enqueue({score:y,part:{heatmapY:s,heatmapX:h,id:i}})}return _}var he=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ie=he.length,we=he.reduce((t,e,n)=>(t[e]=n,t),{}),H0=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Mr=H0.map(([t,e])=>[we[t],we[e]]),zt=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function W1(t,e,n,c){return{y:c.get(t,e,n),x:c.get(t,e,n+ie)}}function Ze(t,e,n){let{heatmapY:c,heatmapX:r,id:A}=t,{y:_,x:s}=W1(c,r,A,n);return{x:t.heatmapX*e+s,y:t.heatmapY*e+_}}function N1(t,e,n){return t<e?e:t>n?n:t}function Mt(t,e,n,c){let r=n-t,A=c-e;return r*r+A*A}function k1(t,e){return{x:t.x+e.x,y:t.y+e.y}}function Rt(t,e){let n=e.shape[0],c=new Float32Array(n);for(let r=0;r<n;r++){let A=e.get(r,0),_=e.get(r,1);c[r]=t.get(A,_,r)}return c}function X0(t,e,n,c){return{y:c.get(t,e,n),x:c.get(t,e,n+ie)}}function F0(t,e){let n=[];for(let c=0;c<ie;c++){let r=t.get(c,0).valueOf(),A=t.get(c,1).valueOf(),{x:_,y:s}=X0(r,A,c,e);n.push(s),n.push(_)}return o.tensor2d(n,[ie,2])}function Et(t,e,n){return o.tidy(()=>t.toTensor().mul(o.scalar(e,"int32")).toFloat().add(F0(t,n)))}function q0(t,e){return o.tidy(()=>{let n=t.div(o.scalar(e,"int32"));return t.sub(n.mul(o.scalar(e,"int32")))})}function St(t){let[e,n,c]=t.shape;return o.tidy(()=>{let 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Ot(t,e,n){let c=0,r=St(t),A=await Promise.all([t.buffer(),e.buffer(),r.buffer()]),_=A[0],s=A[1],h=A[2],i=Et(h,C0,s),y=await i.buffer(),l=Array.from(Rt(_,h)).map((m,x)=>(c+=m,{position:{y:y.get(x,0),x:y.get(x,1)},part:he[x],score:m})),p=l.filter(m=>m.score>n);return r.dispose(),i.dispose(),{keypoints:p,score:c/l.length}}var Y0=1,It=16;function Zt(t,e,{x:n,y:c},r){return t.some(({keypoints:A})=>{let _=A[r].position;return Mt(c,n,_.y,_.x)<=e})}function J0(t,e,n){return n.reduce((r,{position:A,score:_},s)=>(Zt(t,e,A,s)||(r+=_),r),0)/n.length}function Lt(t,e,n,c,r,A,_){let s=[],h=vt(_,Y0,t),i=r^2;for(;s.length<A&&!h.empty();){let y=h.dequeue(),a=Ze(y.part,It,e);if(Zt(s,i,a,y.part.id))continue;let l=kt(y,t,e,It,n,c),p=J0(s,i,l);p>_&&s.push({keypoints:l,score:p})}return s}async function Vt(t){return Promise.all(t.map(e=>e.buffer()))}function K0(t,e,n){return{score:t.score,keypoints:t.keypoints.map(({score:c,part:r,position:A})=>({score:c,part:r,position:{x:Math.trunc(A.x*n),y:Math.trunc(A.y*e)}}))}}function Ht(t,[e,n]){let c=t.squeeze(0),r=c.resizeBilinear([e,n]);return c.dispose(),r}function Z1(t,[e,n],[c,r]){return t.map(_=>K0(_,e/c,n/r))}async function U0(t,e,n,c){return new Promise(async r=>{let A=await Vt([e.heatmapScores,e.offsets,e.displacementFwd,e.displacementBwd]),_=A[0],s=A[1],h=A[2],i=A[3],y=await Lt(_,s,h,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),a=Z1(y,[t.shape[1],t.shape[2]],[c,c]);r(a)})}async function D0(t,e,n,c){return new Promise(async r=>{let A=await Ot(e.heatmapScores,e.offsets,n.body.scoreThreshold),_=Z1([A],[t.shape[1],t.shape[2]],[c,c]);r(_)})}var V1=class{constructor(e){this.baseModel=e,this.inputSize=e.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(e,n){let c=Ht(e,[this.inputSize,this.inputSize]),r=this.baseModel.predict(c,n),A=n.body.maxDetections<2?await D0(e,r,n,this.inputSize):await U0(e,r,n,this.inputSize);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),c.dispose(),A}dispose(){this.baseModel.dispose()}};async function H1(t){let e=await o.loadGraphModel(t.body.modelPath),n=new E1(e);return t.debug&&g(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`),new V1(n)}var B1={};X(B1,{HandPose:()=>J1,load:()=>K1});function Le(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function le(t){return[t.startPoint[0]+(t.endPoint[0]-t.startPoint[0])/2,t.startPoint[1]+(t.endPoint[1]-t.startPoint[1])/2]}function Xt(t,e,n){let c=e.shape[1],r=e.shape[2],A=[[t.startPoint[1]/c,t.startPoint[0]/r,t.endPoint[1]/c,t.endPoint[0]/r]];return o.image.cropAndResize(e,A,[0],n)}function Ft(t,e){let n=[t.startPoint[0]*e[0],t.startPoint[1]*e[1]],c=[t.endPoint[0]*e[0],t.endPoint[1]*e[1]],r=t.palmLandmarks.map(A=>[A[0]*e[0],A[1]*e[1]]);return{startPoint:n,endPoint:c,palmLandmarks:r,confidence:t.confidence}}function Ve(t,e=1.5){let n=le(t),c=Le(t),r=[e*c[0]/2,e*c[1]/2],A=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:A,endPoint:_,palmLandmarks:t.palmLandmarks}}function He(t){let e=le(t),n=Le(t),r=Math.max(...n)/2,A=[e[0]-r,e[1]-r],_=[e[0]+r,e[1]+r];return{startPoint:A,endPoint:_,palmLandmarks:t.palmLandmarks}}var X1=class{constructor(e,n,c){this.model=e,this.anchors=c.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=o.tensor2d(this.anchors),this.inputSize=n,this.inputSizeTensor=o.tensor1d([n,n]),this.doubleInputSizeTensor=o.tensor1d([n*2,n*2])}normalizeBoxes(e){return o.tidy(()=>{let 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n=e.map(_=>_[0]),c=e.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A}}};var 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Dt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],Gt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var V;async function D1(t){return V||(V=await o.loadGraphModel(t.body.modelPath),V.width=parseInt(V.signature.inputs["input_1:0"].tensorShape.dim[2].size),V.height=parseInt(V.signature.inputs["input_1:0"].tensorShape.dim[1].size),t.debug&&g(`load model: ${t.body.modelPath.match(/\/(.*)\./)[1]}`)),V}async function G1(t,e){if(!V||!e.body.enabled)return null;let n={width:t.shape[2],height:t.shape[1]},c=o.image.resizeBilinear(t,[V.width,V.height],!1),r=o.div(c,[255]);c.dispose();let A;if(e.profile){let i=await o.profile(()=>V.predict(r));A=i.result.find(y=>y.size===195||y.size===155).dataSync(),i.result.forEach(y=>y.dispose()),k("blazepose",i)}else{let i=await V.predict(r);A=i.find(y=>y.size===195||y.size===155).dataSync(),i.forEach(y=>y.dispose())}r.dispose();let _=[],s=A.length===195?Dt:Gt,h=5;for(let 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i;if(N.data){let a=[N.height,N.width,3];i=o.tensor3d(N.data,a,"int32")}else if(e.backend==="webgl"||N instanceof ImageData)i=o.browser.fromPixels(N);else{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(_,s):document.createElement("canvas");a.width=_,a.height=s;let l=a.getContext("2d");l==null||l.drawImage(N,0,0);let p=l==null?void 0:l.getImageData(0,0,_,s);i=o.browser.fromPixels(p)}let y=i.toFloat();n=y.expandDims(0),i.dispose(),y.dispose()}let c=e.filter.return?N:null;return{tensor:n,canvas:c}}var At={};X(At,{all:()=>on,body:()=>A0,canvas:()=>_n,drawOptions:()=>d,face:()=>c0,gesture:()=>r0,hand:()=>_0,object:()=>o0});var E={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var 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A=[],_=[];if([A,_]=Object.entries(e[r]),_.length>1&&_[1].length>0){let s=A[1]>0?`#${A[1]}`:"",h=`${A[0]} ${s}: ${_[1]}`;d.shadowColor&&d.shadowColor!==""&&(n.fillStyle=d.shadowColor,n.fillText(h,8,2+c*d.lineHeight)),n.fillStyle=d.labelColor,n.fillText(h,6,0+c*d.lineHeight),c+=1}}}async function c0(t,e){if(!e||!t||!(t instanceof HTMLCanvasElement))return;let n=t.getContext("2d");if(!!n)for(let c of e){n.font=d.font,n.strokeStyle=d.color,n.fillStyle=d.color,d.drawBoxes&&(d.useRawBoxes?ae(n,t.width*c.boxRaw[0],t.height*c.boxRaw[1],t.width*c.boxRaw[2],t.height*c.boxRaw[3]):ae(n,c.box[0],c.box[1],c.box[2],c.box[3]));let r=[];if(r.push(`face confidence: ${Math.trunc(100*c.confidence)}%`),c.genderConfidence&&r.push(`${c.gender||""} ${Math.trunc(100*c.genderConfidence)}% confident`),c.age&&r.push(`age: ${c.age||""}`),c.iris&&r.push(`iris distance: ${c.iris}`),c.emotion&&c.emotion.length>0){let A=c.emotion.map(_=>`${Math.trunc(100*_.score)}% ${_.emotion}`);r.push(A.join(" 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backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&g("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let c=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),r=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&g(`wasm execution: ${c?"SIMD":"no SIMD"} ${r?"multithreaded":"singlethreaded"}`),c||g("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&it();try{await this.tf.setBackend(this.config.backend)}catch(c){g("error: cannot set backend:",this.config.backend,c)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(g("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let c=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&g(`gl version:${c.getParameter(c.VERSION)} renderer:${c.getParameter(c.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-n)}});Ke.set(this,async()=>{let e=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(_=>_.blob()),n,c;switch(this.config.warmup){case"face":n=await e(Ce);break;case"full":n=await e(Ye);break;default:n=null}if(n){let r=await createImageBitmap(n);c=await this.detect(r,this.config),r.close()}return c});Ue.set(this,async()=>new Promise(e=>{let n,c=0;switch(this.config.warmup){case"face":c=256,n="data:image/jpeg;base64,"+Ce;break;case"full":case"body":c=1200,n="data:image/jpeg;base64,"+Ye;break;default:n=null}let r=new Image;r.onload=async()=>{let A=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(c,c):document.createElement("canvas");A.width=r.naturalWidth,A.height=r.naturalHeight;let _=A.getContext("2d");_==null||_.drawImage(r,0,0);let s=await this.detect(A,this.config);e(s)},n?r.src=n:e(null)}));De.set(this,async()=>{let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(Ce):e(Ye),c=o.node.decodeJpeg(n),r=c.expandDims(0);this.tf.dispose(c);let A=await this.detect(r,this.config);return this.tf.dispose(r),A});this.tf=o,this.draw=tt,this.version=o0,this.config=re(E,e),this.state="idle",$(this,ae,0),$(this,le,!1),$(this,de,!1),$(this,ne,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>et(n,this.config),this.classes={facemesh:g1,age:Qe,gender:e1,emotion:A1,faceres:a1,body:this.config.body.modelPath.includes("posenet")?k1:C1,hand:X1,nanodet:D1},this.sysinfo=ht()}profileData(){return this.config.profile?Ge:{}}similarity(e,n){return this.config.face.description.enabled?y1(e,n):this.config.face.embedding.enabled?at(e,n):0}enhance(e){return w1(e)}match(e,n,c=0){return xt(e,n,c)}async load(e={}){this.state="load";let n=v();e&&(this.config=re(this.config,e)),L(this,ne)&&(this.config.debug&&g(`version: ${this.version}`),this.config.debug&&g(`tfjs version: ${this.tf.version_core}`),this.config.debug&&g("platform:",this.sysinfo.platform),this.config.debug&&g("agent:",this.sysinfo.agent),await L(this,fe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&g("configuration:",this.config),this.config.debug&&g("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?P1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?$e(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?c1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?s1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?h1(this.config):null),this.models.handpose||(this.config.hand.enabled?B1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?I1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?Y1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?K1(this.config):null),this.models.nanodet||(this.config.object.enabled?Q1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?x1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await P1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await $e(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await c1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await s1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await h1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await B1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await I1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await Y1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await K1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await Q1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await x1(this.config))),L(this,ne)&&(this.config.debug&&g("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),$(this,ne,!1));let c=Math.trunc(v()-n);c>(this.perf.load||0)&&(this.perf.load=c)}async detect(e,n={}){return new Promise(async c=>{var y,z,T,S;this.state="config";let r;this.config=re(this.config,n),this.state="check";let A=L(this,Je).call(this,e);A&&(g(A,e),c({error:A}));let _=v();await L(this,fe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=v();let s=et(e,this.config);if(!s||!s.tensor){g("could not convert input to tensor"),c({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-r),this.analyze("Get Image:");let h,a,x,i,l;this.config.async?(x=this.config.face.enabled?l1(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=v(),x=this.config.face.enabled?await l1(this,s.tensor):[],l=Math.trunc(v()-r),l>0&&(this.perf.face=l)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?h=this.config.body.enabled?(y=this.models.posenet)==null?void 0:y.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?h=this.config.body.enabled?J1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(h=this.config.body.enabled?U1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=v(),this.config.body.modelPath.includes("posenet")?h=this.config.body.enabled?await((z=this.models.posenet)==null?void 0:z.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?h=this.config.body.enabled?await J1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(h=this.config.body.enabled?await U1(s.tensor,this.config):[]),l=Math.trunc(v()-r),l>0&&(this.perf.body=l)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=v(),a=this.config.hand.enabled?await((S=this.models.handpose)==null?void 0:S.estimateHands(s.tensor,this.config)):[],l=Math.trunc(v()-r),l>0&&(this.perf.hand=l)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(i=this.config.object.enabled?$1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=v(),i=this.config.object.enabled?await $1(s.tensor,this.config):[],l=Math.trunc(v()-r),l>0&&(this.perf.object=l)),this.analyze("End Object:"),this.config.async&&([x,h,a,i]=await Promise.all([x,h,a,i])),s.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let m=[];this.config.gesture.enabled&&(r=v(),m=[...Qt(x),...Gt(h),...e0(a),...$t(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-r)),this.perf.total=Math.trunc(v()-_),this.state="idle";let p={face:x,body:h,hand:a,gesture:m,object:i,performance:this.perf,canvas:s.canvas};c(p)})}async warmup(e={}){let n=v();e&&(this.config=re(this.config,e));let c=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await L(this,Ke).call(this):typeof Image!="undefined"?r=await L(this,Ue).call(this):r=await L(this,De).call(this),this.config.videoOptimized=c;let A=v();return this.config.debug&&g("Warmup",this.config.warmup,Math.round(A-n),"ms",r),r}};ae=new WeakMap,le=new WeakMap,de=new WeakMap,ne=new WeakMap,Je=new WeakMap,fe=new WeakMap,Ke=new WeakMap,Ue=new WeakMap,De=new WeakMap;export{hn as Human,hn as default};
+2Q==`;var s0="1.2.5";var xe,de,fe,re,Ge,me,Qe,$e,e1,sn=class{constructor(e={}){xe.set(this,void 0);de.set(this,void 0);fe.set(this,void 0);re.set(this,void 0);this.analyze=(...e)=>{if(!L(this,de))return;let n=this.tf.engine().state.numTensors,c=L(this,xe);ee(this,xe,n);let r=n-c;r!==0&&g(...e,r)};Ge.set(this,e=>{if(!L(this,fe))return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof o.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});me.set(this,async(e=!1)=>{if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let n=z();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&g("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&g("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let c=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),r=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&g(`wasm execution: ${c?"SIMD":"no SIMD"} ${r?"multithreaded":"singlethreaded"}`),c||g("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&yt();try{await this.tf.setBackend(this.config.backend)}catch(c){g("error: cannot set backend:",this.config.backend,c)}}if(this.tf.enableProdMode(),this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.tf.getBackend()==="webgl"){this.config.deallocate&&(g("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let c=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&g(`gl version:${c.getParameter(c.VERSION)} renderer:${c.getParameter(c.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(z()-n)}});Qe.set(this,async()=>{let e=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(_=>_.blob()),n,c;switch(this.config.warmup){case"face":n=await e(Ue);break;case"full":n=await e(De);break;default:n=null}if(n){let r=await createImageBitmap(n);c=await this.detect(r,this.config),r.close()}return c});$e.set(this,async()=>new Promise(e=>{let n,c=0;switch(this.config.warmup){case"face":c=256,n="data:image/jpeg;base64,"+Ue;break;case"full":case"body":c=1200,n="data:image/jpeg;base64,"+De;break;default:n=null}let r=new Image;r.onload=async()=>{let A=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(c,c):document.createElement("canvas");A.width=r.naturalWidth,A.height=r.naturalHeight;let _=A.getContext("2d");_==null||_.drawImage(r,0,0);let s=await this.detect(A,this.config);e(s)},n?r.src=n:e(null)}));e1.set(this,async()=>{let e=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?e(Ue):e(De),c=o.node.decodeJpeg(n),r=c.expandDims(0);this.tf.dispose(c);let A=await this.detect(r,this.config);return this.tf.dispose(r),A});this.tf=o,this.draw=At,this.version=s0,this.config=ce(E,e),this.state="idle",ee(this,xe,0),ee(this,de,!1),ee(this,fe,!1),ee(this,re,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>ct(n,this.config),this.classes={facemesh:z1,age:n1,gender:c1,emotion:h1,faceres:l1,body:this.config.body.modelPath.includes("posenet")?L1:U1,hand:B1,nanodet:et},this.sysinfo=xt()}profileData(){return this.config.profile?ue:{}}similarity(e,n){return this.config.face.description.enabled?f1(e,n):this.config.face.embedding.enabled?wt(e,n):0}enhance(e){return m1(e)}match(e,n,c=0){return lt(e,n,c)}async load(e={}){this.state="load";let n=z();e&&(this.config=ce(this.config,e)),L(this,re)&&(this.config.debug&&g(`version: ${this.version}`),this.config.debug&&g(`tfjs version: ${this.tf.version_core}`),this.config.debug&&g("platform:",this.sysinfo.platform),this.config.debug&&g("agent:",this.sysinfo.agent),await L(this,me).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&g("configuration:",this.config),this.config.debug&&g("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?R1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?r1(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?s1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?x1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?y1(this.config):null),this.models.handpose||(this.config.hand.enabled?K1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?H1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?D1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Q1(this.config):null),this.models.nanodet||(this.config.object.enabled?nt(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?d1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await R1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await r1(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await s1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await x1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await y1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await K1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await H1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await D1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Q1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await nt(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await d1(this.config))),L(this,re)&&(this.config.debug&&g("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ee(this,re,!1));let c=Math.trunc(z()-n);c>(this.perf.load||0)&&(this.perf.load=c)}async detect(e,n={}){return new Promise(async c=>{var x,P,T,M;this.state="config";let r;this.config=ce(this.config,n),this.state="check";let A=L(this,Ge).call(this,e);A&&(g(A,e),c({error:A}));let _=z();await L(this,me).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=z();let s=ct(e,this.config);if(!s||!s.tensor){g("could not convert input to tensor"),c({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(z()-r),this.analyze("Get Image:");let h,i,y,a,l;this.config.async?(y=this.config.face.enabled?p1(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=z(),y=this.config.face.enabled?await p1(this,s.tensor):[],l=Math.trunc(z()-r),l>0&&(this.perf.face=l)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?h=this.config.body.enabled?(x=this.models.posenet)==null?void 0:x.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?h=this.config.body.enabled?G1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(h=this.config.body.enabled?$1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=z(),this.config.body.modelPath.includes("posenet")?h=this.config.body.enabled?await((P=this.models.posenet)==null?void 0:P.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?h=this.config.body.enabled?await G1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(h=this.config.body.enabled?await $1(s.tensor,this.config):[]),l=Math.trunc(z()-r),l>0&&(this.perf.body=l)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(i=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=z(),i=this.config.hand.enabled?await((M=this.models.handpose)==null?void 0:M.estimateHands(s.tensor,this.config)):[],l=Math.trunc(z()-r),l>0&&(this.perf.hand=l)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(a=this.config.object.enabled?rt(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=z(),a=this.config.object.enabled?await rt(s.tensor,this.config):[],l=Math.trunc(z()-r),l>0&&(this.perf.object=l)),this.analyze("End Object:"),this.config.async&&([y,h,i,a]=await Promise.all([y,h,i,a])),s.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(r=z(),p=[...$t(y),...Qt(h),...t0(i),...e0(y)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(z()-r)),this.perf.total=Math.trunc(z()-_),this.state="idle";let m={face:y,body:h,hand:i,gesture:p,object:a,performance:this.perf,canvas:s.canvas};c(m)})}async warmup(e={}){let n=z();e&&(this.config=ce(this.config,e));let c=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await L(this,Qe).call(this):typeof Image!="undefined"?r=await L(this,$e).call(this):r=await L(this,e1).call(this),this.config.videoOptimized=c;let A=z();return this.config.debug&&g("Warmup",this.config.warmup,Math.round(A-n),"ms",r),r}};xe=new WeakMap,de=new WeakMap,fe=new WeakMap,re=new WeakMap,Ge=new WeakMap,me=new WeakMap,Qe=new WeakMap,$e=new WeakMap,e1=new WeakMap;export{sn as Human,sn as default};
 //# sourceMappingURL=human.esm-nobundle.js.map
diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map
index ddd72c19..a74bbd83 100644
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/tf-browser.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/imagefx.js", "../src/image/image.ts", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts", "../src/human.ts"],
-  "sourcesContent": ["// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(name: string, raw: any): void {\n  if (!raw || !raw.kernels) return;\n  const maxResults = 5;\n  const time = raw.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n  data[name] = res;\n  log('Human profiler', name, res);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\nconst activateScore = false;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position\n          if (score > config.object.minConfidence) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            let boxRaw = [ // results normalized to range 0..1\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n              cx + (scaleBox / strideSize * boxOffset[2]),\n              cy + (scaleBox / strideSize * boxOffset[3]),\n            ];\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n  const nmsIdx = nms.dataSync();\n  tf.dispose(nms);\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    // @ts-ignore\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    resize.dispose();\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result.clone();\n      profileObject.result.dispose();\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      tf.enableDebugMode();\n      */\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
-  "mappings": 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+  "sourcesContent": ["// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 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0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    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{\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.executeAsync(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      this.tf.enableDebugMode();\n      */\n      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
+  "mappings": 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 }
diff --git a/dist/human.esm.js b/dist/human.esm.js
index e22e185c..a6ba5019 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -5,31 +5,31 @@
   author: <https://github.com/vladmandic>'
   */
 
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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;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new gk(this.backendInstance),!0}setupRegisteredKernels(){Jo(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jo(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof uu)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,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(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),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 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=gf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(gf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=td(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=gf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=cf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}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 a=e;n==="string"&&ba(e[0])&&(a=e.map(o=>Ru(o)));let s=r.write(a,t,n),i=new Ve(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Xg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ve(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Du(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*nf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Du||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*nf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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(){let 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;let 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(let r of 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+`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function $u(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var $t=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Ft(e),n!=null){let r=n.length;M(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Gg(t,this.size),this.strides=Ki(e)}set(e,...t){t.length===0&&(t=[0]),M(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}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];let 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 Cr().makeTensor(this.values,this.shape,this.dtype)}},Cr=null,Qo=null,_k=null;function vk(e){Cr=e}function kk(e){Qo=e}function Ik(e){_k=e}var Ve=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ft(e),this.strides=Ki(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Qo.buffer(this.shape,this.dtype,e)}bufferSync(){return Qo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Zi(this.shape,e)}arraySync(){return Zi(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Cr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>rd(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();let e=Cr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>rd(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();let e=await Cr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Cr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Qo.print(this,e)}clone(){return this.throwIfDisposed(),Qo.clone(this)}toString(e=!1){let t=this.dataSync();return bk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Qo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cr().makeVariable(this,e,t,n)}};Object.defineProperty(Ve,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return cf("Tensor",()=>Ve)}Z();var Du=class extends Ve{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(!ea(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Cr().disposeTensor(this),this.dataId=e.dataId,Cr().incRef(this,null)}dispose(){Cr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Du,Symbol.hasInstance,{value:e=>e instanceof Ve&&e.assign!=null&&e.assign instanceof Function});var mr={};Oe(mr,{assertTypesMatch:()=>o5,getTensorsInContainer:()=>ff,isTensorInList:()=>Nk,makeTypesMatch:()=>bt});var mf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(mf||(mf={}));var Af;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Af||(Af={}));var yf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(yf||(yf={}));var gf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(gf||(gf={}));var xf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(xf||(xf={}));var Sk={float32:gf,int32:Af,bool:yf,complex64:xf};function tr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Sk[e][t]}function sd(e){return tr(e,"int32")}function bt(e,t){if(e.dtype===t.dtype)return[e,t];let n=tr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function o5(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Nk(e,t){return t.some(n=>n.id===e.id)}function ff(e){let t=[],n=new Set;return l5(e,t,n),t}function l5(e,t,n){if(e==null)return;if(e instanceof Ve){t.push(e);return}if(!Tk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),l5(s,t,n))}}function Tk(e){return Array.isArray(e)||typeof e=="object"}function wf(e){return e.kernelName!=null}var u5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Ou=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new u5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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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;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new yk(this.backendInstance),!0}setupRegisteredKernels(){Jo(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jo(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof uu)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,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(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),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 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=wf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(wf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=td(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:u,attrs:c}=e,h=wf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=df(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}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 a=e;n==="string"&&ba(e[0])&&(a=e.map(o=>Ru(o)));let s=r.write(a,t,n),i=new Ve(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Kg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ve(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Du(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*af(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Du||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*af(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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(){let 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;let 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(let r of 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n.makeTensorInfo(a.shape,a.dtype,m)}var MM={kernelName:bs,backendName:"cpu",kernelFunc:RM};function FM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=At({inputs:{x:a},backend:n,attrs:{shape:l}}),f=sr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=At({inputs:{x:f},backend:n,attrs:{shape:u}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var $M={kernelName:fu,backendName:"cpu",kernelFunc:FM};function DM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Em(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var 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fF={kernelName:Fh,backendName:"cpu",kernelFunc:pF};function mF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;ve([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new $t(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,w=n.data.get(a.dataId).values,[b,_,x]=h,N=n.data.get(s.dataId).values,[T,C,F]=d,{batchSize:P,filterHeight:W,filterWidth:V,inChannels:U,inHeight:H,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,oe=W-1-p.padInfo.top,Q=V-1-p.padInfo.left,he=G/U;for(let le=0;le<P;++le)for(let fe=0;fe<U;++fe)for(let pe=0;pe<H;++pe){let ke=pe-oe,Se=Math.max(0,Math.ceil(ke/ae)),Me=Math.min(ee,(W+ke)/ae);for(let De=0;De<X;++De){let Fe=De-Q,Qe=Math.max(0,Math.ceil(Fe/te)),et=Math.min(Y,(V+Fe)/te),st=0;for(let Ke=Se;Ke<Me;++Ke){let ht=Ke*ae-ke;for(let Be=Qe;Be<et;++Be){let 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n.makeTensorInfo(a.shape,a.dtype,m)}var RM={kernelName:bs,backendName:"cpu",kernelFunc:EM};function MM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=At({inputs:{x:a},backend:n,attrs:{shape:l}}),f=sr({inputs:{x:p},backend:n,attrs:{perm:u}}),m=At({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var FM={kernelName:fu,backendName:"cpu",kernelFunc:MM};function $M(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Mm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var DM={kernelName:Sh,backendName:"cpu",kernelFunc:$M},OM=at(ka,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),zM={kernelName:ka,backendName:"cpu",kernelFunc:OM},PM=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let c=o[u],h=l[u];r[u]=Math.hypot(c,h)}return n.makeOutput(r,t.shape,"float32")},LM={kernelName:mu,backendName:"cpu",kernelFunc:PM};function _l(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var WM={kernelName:Bh,backendName:"cpu",kernelFunc:_l};function vl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(i)===0)return 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GM={kernelName:ps,backendName:"cpu",kernelFunc:HM};function qM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;ve([a,s],"conv3d");let u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:c,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=u,y=A.front,g=A.left,w=A.top,b=new $t(u.outShape,a.dtype),_=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=b.values,T=v.computeStrides(a.shape),C=v.computeStrides(s.shape);for(let F=0;F<u.batchSize;++F){let P=F*T[0],W=F*b.strides[0];for(let V=0;V<u.outDepth;++V){let U=W+V*b.strides[1],j=V*u.strideDepth-y;for(let X=0;X<c;++X){let G=j+X*p;if(G<0||G>=u.inDepth)continue;let ee=X*C[0],Y=P+G*T[1];for(let ae=0;ae<u.outHeight;++ae){let te=U+ae*b.strides[2],oe=ae*u.strideHeight-w;for(let Q=0;Q<h;++Q){let he=oe+Q*f;if(he<0||he>=u.inHeight)continue;let le=ee+Q*C[1],fe=Y+he*T[2];for(let pe=0;pe<u.outWidth;++pe){let ke=te+pe*u.outChannels,Se=pe*u.strideWidth-g;for(let 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pF={kernelName:Fh,backendName:"cpu",kernelFunc:dF};function fF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r;ve([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),f=new $t(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,w=n.data.get(a.dataId).values,[b,_,x]=h,N=n.data.get(s.dataId).values,[T,C,F]=d,{batchSize:P,filterHeight:W,filterWidth:V,inChannels:U,inHeight:j,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,oe=W-1-p.padInfo.top,Q=V-1-p.padInfo.left,he=G/U;for(let le=0;le<P;++le)for(let fe=0;fe<U;++fe)for(let pe=0;pe<j;++pe){let ke=pe-oe,Se=Math.max(0,Math.ceil(ke/ae)),Me=Math.min(ee,(W+ke)/ae);for(let De=0;De<X;++De){let Fe=De-Q,Qe=Math.max(0,Math.ceil(Fe/te)),et=Math.min(Y,(V+Fe)/te),st=0;for(let Ke=Se;Ke<Me;++Ke){let ht=Ke*ae-ke;for(let Be=Qe;Be<et;++Be){let 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+`)[0]),console.log(`%c ${v.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
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e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function oa(e,t,n){let r=we(e,()=>t());if(r==null)throw new Error(n);return r}function ub(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function yi(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function gi(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function ap(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[yi(e),...gi(e)]),t}function nb(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?v.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let 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ab(e){if(lp==null){let t=Wr(e);lp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,lp)}function sb(e){if(e===0)return 0;let t,n=Wr(e);return qn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:qn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function qn(e,t){return e.getExtension(t)!=null}function Xm(e){try{if(Wr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function ib(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!qn(t,"OES_texture_float"))return!1}else if(!qn(t,"EXT_color_buffer_float"))return!1;return Ym(t)}function ob(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!qn(t,"OES_texture_float")||!qn(t,"WEBGL_color_buffer_float"))return!1}else{if(qn(t,"EXT_color_buffer_float"))return Ym(t);let n="EXT_color_buffer_half_float";if(qn(t,n)){let r=t.getExtension(n);return YO(t,r)}return!1}return Ym(t)}function Ym(e){let t=Zm(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function YO(e,t){let n=Zm(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function lb(e){return e!==2?!1:Wr(e).fenceSync!=null}function kl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Re=J();Re.registerFlag("HAS_WEBGL",()=>Re.getNumber("WEBGL_VERSION")>0);Re.registerFlag("WEBGL_VERSION",()=>Xm(2)?2:Xm(1)?1:0);Re.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Re.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Re.get("WEBGL_VERSION")===2);Re.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Re.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Re.registerFlag("WEBGL_PACK",()=>Re.getBool("HAS_WEBGL"));Re.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_CLIP",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Re.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_REDUCE",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_LAZILY_UNPACK",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_CONV_IM2COL",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>rb(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>ab(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Re.getNumber("WEBGL_VERSION");return e===0?0:sb(e)});Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Re.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!zu.isMobile());Re.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>ib(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Re.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Re.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Re.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>ob(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_FENCE_API_ENABLED",()=>lb(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Re.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Re.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Re.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>zu.isMobile()&&Re.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function hn(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -40,7 +40,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       }
 
       #define isnan(value) isnan_custom(value)
-    `,l="",c=`
+    `,l="",u=`
       #define round(value) newRound(value)
       int newRound(float value) {
         return int(floor(value + 0.5));
@@ -66,7 +66,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isinf(vec4 val) {
         return equal(abs(val), vec4(INFINITY));
       }
-    `,c=`
+    `,u=`
       int round(float value) {
         return int(floor(value + 0.5));
       }
@@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function wi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Zm(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function wi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Jm(e){let t=v.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var ub=`
+`}var cb=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -117,7 +117,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,QO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
+`,JO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${wi(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -138,7 +138,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},ez=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
+    `}},QO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${wi(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},tz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
-      ${ub}
+    `}},ez=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
+      ${cb}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},nz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
-      ${ub}
+    `}},tz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
+      ${cb}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},rz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=hn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${Zm(e)}
+    `}},nz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=hn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${Jm(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -204,10 +204,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},az=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=hn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
+    `}},rz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=hn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=`
           localCoords = coords;
-          if(localCoords[2] + ${c} < ${e[2]}) {
-            localCoords[2] += ${c};
+          if(localCoords[2] + ${u} < ${e[2]}) {
+            localCoords[2] += ${u};
             if(localCoords[1] + ${l} < ${e[1]}) {
               localCoords[1] += ${l};
 
@@ -222,18 +222,18 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
               values = ${r.texture2D}(A, uv);
 
               if(offset == 0) {
-                result[${u}] = values[0];
+                result[${c}] = values[0];
               } else if(offset == 1) {
-                result[${u}] = values[1];
+                result[${c}] = values[1];
               } else if(offset == 2) {
-                result[${u}] = values[2];
+                result[${c}] = values[2];
               } else {
-                result[${u}] = values[3];
+                result[${c}] = values[3];
               }
             }
           }
         `}this.userCode=`
-      ${Zm(e)}
+      ${Jm(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = ${o};
       }
-    `}},cb={};Oe(cb,{bindVertexProgramAttributeStreams:()=>xb,createBufferFromOutputTexture:()=>_b,createFloat16MatrixTexture:()=>mb,createFloat16PackedMatrixTexture:()=>gb,createFloat32MatrixTexture:()=>fb,createIndexBuffer:()=>pb,createPackedMatrixTexture:()=>yb,createUnsignedBytesMatrixTexture:()=>Ab,createVertexBuffer:()=>db,createVertexShader:()=>hb,downloadByteEncodedFloatMatrixFromOutputTexture:()=>kb,downloadFloat32MatrixFromBuffer:()=>vb,downloadMatrixFromPackedOutputTexture:()=>Nb,downloadPackedMatrixFromBuffer:()=>Ib,getInternalFormatForFloat16MatrixTexture:()=>Jm,getInternalFormatForFloat16PackedMatrixTexture:()=>tA,getInternalFormatForFloat32MatrixTexture:()=>Ym,getInternalFormatForPackedMatrixTexture:()=>eA,getInternalFormatForUnsignedBytesMatrixTexture:()=>Qm,uploadDenseMatrixToTexture:()=>wb,uploadPixelDataToTexture:()=>bb});function hb(e){let t=hn(),n=`${t.version}
+    `}},hb={};Oe(hb,{bindVertexProgramAttributeStreams:()=>wb,createBufferFromOutputTexture:()=>vb,createFloat16MatrixTexture:()=>Ab,createFloat16PackedMatrixTexture:()=>xb,createFloat32MatrixTexture:()=>mb,createIndexBuffer:()=>fb,createPackedMatrixTexture:()=>gb,createUnsignedBytesMatrixTexture:()=>yb,createVertexBuffer:()=>pb,createVertexShader:()=>db,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Ib,downloadFloat32MatrixFromBuffer:()=>kb,downloadMatrixFromPackedOutputTexture:()=>Sb,downloadPackedMatrixFromBuffer:()=>Nb,getInternalFormatForFloat16MatrixTexture:()=>eA,getInternalFormatForFloat16PackedMatrixTexture:()=>rA,getInternalFormatForFloat32MatrixTexture:()=>Qm,getInternalFormatForPackedMatrixTexture:()=>nA,getInternalFormatForUnsignedBytesMatrixTexture:()=>tA,uploadDenseMatrixToTexture:()=>bb,uploadPixelDataToTexture:()=>_b});function db(e){let t=hn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return Bw(e,n)}function db(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Hw(e,t)}function pb(e){let t=new Uint16Array([0,1,2,2,1,3]);return Gw(e,t)}function dc(e,t,n,r,a,s){Xw(t,n);let i=qw(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Ym(e){return e.internalFormatFloat}function fb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Ym(r),r.textureFormatFloat,e.FLOAT)}function Jm(e){return e.internalFormatHalfFloat}function mb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Jm(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function Qm(e){return e.downloadTextureFormat}function Ab(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Qm(r),e.RGBA,e.UNSIGNED_BYTE)}function eA(e){return e.internalFormatPackedFloat}function yb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,eA(r),e.RGBA,e.FLOAT)}function tA(e){return e.internalFormatPackedHalfFloat}function gb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,tA(r),e.RGBA,r.textureTypeHalfFloat)}function xb(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),jm(e,t,"clipSpacePos",n,3,s,r)&&jm(e,t,"uv",n,2,s,a)}function wb(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function bb(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _b(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function vb(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function kb(e,t,n,r){let[a,s]=cc(t,n),i=4,o=new Uint8Array(HO(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Ib(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(GO(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function Nb(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var up=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ic(this.gl,a),qn(this.gl,s))this.textureHalfFloatExtension=ic(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),qn(this.gl,r))this.colorBufferHalfFloatExtension=ic(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",qn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(qn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=db(this.gl),this.indexBuffer=pb(this.gl),this.framebuffer=Kw(this.gl),this.textureConfig=Xm(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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r=this.gl;rp(r,e,this.framebuffer),this.debug&&oc(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function sz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Sb}=R;function fz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
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+    }`;return Vw(e,n)}function pb(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Gw(e,t)}function fb(e){let t=new Uint16Array([0,1,2,2,1,3]);return qw(e,t)}function dc(e,t,n,r,a,s){Kw(t,n);let i=Xw(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Qm(e){return e.internalFormatFloat}function mb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Qm(r),r.textureFormatFloat,e.FLOAT)}function eA(e){return e.internalFormatHalfFloat}function Ab(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,eA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function tA(e){return e.downloadTextureFormat}function yb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,tA(r),e.RGBA,e.UNSIGNED_BYTE)}function nA(e){return e.internalFormatPackedFloat}function gb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,nA(r),e.RGBA,e.FLOAT)}function rA(e){return e.internalFormatPackedHalfFloat}function xb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,rA(r),e.RGBA,r.textureTypeHalfFloat)}function wb(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Gm(e,t,"clipSpacePos",n,3,s,r)&&Gm(e,t,"uv",n,2,s,a)}function bb(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _b(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function vb(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function kb(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function Ib(e,t,n,r){let[a,s]=cc(t,n),i=4,o=new Uint8Array(jO(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Nb(e,t,n,r,a,s,i,o){let l=e,u=new Float32Array(HO(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Sb(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var up=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ic(this.gl,a),qn(this.gl,s))this.textureHalfFloatExtension=ic(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),qn(this.gl,r))this.colorBufferHalfFloatExtension=ic(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",qn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(qn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=pb(this.gl),this.indexBuffer=fb(this.gl),this.framebuffer=Zw(this.gl),this.textureConfig=Zm(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),xb(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),gb(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(qm(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Ib(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Nb(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kb(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=vb(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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we(t,()=>t.attachShader(a,r)),we(t,()=>t.attachShader(a,n)),Hw(t,a),this.debug&&np(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=wb(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&np(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Jw(this.gl,e,t):Qw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),eb(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=Il(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&np(this.gl,this.program),oc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ic(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=az(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),rp(this.gl,e,this.framebuffer),this.debug&&oc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rp(this.gl,this.outputTexture,this.framebuffer),this.debug&&oc(this.gl)):qm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;rp(r,e,this.framebuffer),this.debug&&oc(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function az(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Tb}=R;function pz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
+`),i=e.map(p=>sz(p,t,r)).join(`
+`),o=t.texShape,l=hn(),u=lz(l),c,h,d=hz(l);return t.isPacked?(c=iz(t.logicalShape,o),h=cz(l)):(c=oz(t.logicalShape,o),h=uz(l)),r&&(d+=dz),[d,u,h,s,c,i,n].join(`
+`)}function Nl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return fz(e);case 1:return mz(e);case 2:return Az(e);case 3:return yz(e);case 4:return gz(e);case 5:return xz(e);case 6:return wz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Cb(e){switch(e.shapeInfo.logicalShape.length){case 0:return bz(e);case 1:return _z(e);case 2:return vz(e);case 3:return kz(e);default:return Iz(e)}}function sz(e,t,n=!1){let r="";n?r+=Cb(e):r+=Nl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Nz(e,t):r+=Sz(e,t)),r}function iz(e,t){switch(e.length){case 0:return Eb();case 1:return Tz(e,t);case 2:return Rz(e,t);case 3:return Cz(e,t);default:return Ez(e,t)}}function oz(e,t){switch(e.length){case 0:return Eb();case 1:return Mz(e,t);case 2:return zz(e,t);case 3:return Fz(e,t);case 4:return $z(e,t);case 5:return Dz(e,t);case 6:return Oz(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function lz(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function cz(e){return`
+  `}function uz(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function hz(e){return`
+  `}function cz(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function dz(e){return`${e.version}
+  `}function hz(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
+    ${Pz}
     ${Lz}
     ${Wz}
-    ${Bz}
-  `}var Lz=`
+  `}var Pz=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Wz=`
+`,Lz=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Bz=`
+`,Wz=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,pz=`
+`,dz=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function Cb(){return`
+`;function Eb(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function Cz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function Tz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${n[1]}.0);
       }
@@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function Fz(e,t){return t[0]===1?`
+  `}function Mz(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function Ez(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
+  `}function Cz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function $z(e,t){let n=wi(["r","c","d"],e);return`
+  `}function Fz(e,t){let n=wi(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function Rz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function Ez(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function Dz(e,t){let n=wi(["r","c","d","d2"],e);return`
+  `}function $z(e,t){let n=wi(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function Oz(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
+  `}function Dz(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function zz(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
+  `}function Oz(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -474,7 +474,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function Mz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
+  `}function Rz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -489,7 +489,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function Pz(e,t){return v.arraysEqual(e,t)?`
+  `}function zz(e,t){return v.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -516,11 +516,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function bi(e){return`offset${e}`}function _z(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=hn();return`
+  `}function bi(e){return`offset${e}`}function bz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=hn();return`
     vec4 ${n}() {
       return ${r.texture2D}(${t}, halfCR);
     }
-  `}function mz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function fz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
@@ -529,13 +529,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function vz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=hn();return`
+  `}function _z(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=hn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${a[0]}, ${a[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function Az(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function mz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
         ${Sl(e)}
       }
@@ -558,18 +558,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function kz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=hn();if(a!=null&&v.arraysEqual(t,a))return`
+  `}function vz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=hn();if(a!=null&&v.arraysEqual(t,a))return`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
         return ${o.texture2D}(${n}, uv);
       }
-    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
+    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(t[1]/2);return`
     vec4 ${r}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
+      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function yz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
+  `}function Az(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
     float ${r}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
       return sampleTexture(${n}, uv);
@@ -584,37 +584,37 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
         ${Sl(e)}
       }
-    `;let l=a[0],c=a[1],u=bi(n);return c===1?`
+    `;let l=a[0],u=a[1],c=bi(n);return u===1?`
     float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
+      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
       return sampleTexture(${n}, uv);
     }
   `:l===1?`
     float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
+      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
       return sampleTexture(${n}, uv);
     }
   `:`
   float ${r}(int row, int col) {
     // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${t[1]} + col + ${u};
-    vec2 uv = uvFromFlat(${l}, ${c}, index);
+    int index = row * ${t[1]} + col + ${c};
+    vec2 uv = uvFromFlat(${l}, ${u}, index);
     return sampleTexture(${n}, uv);
   }
-`}function Iz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Tl(e,h),f=["b","row","col"];return`
-        ${Tb(p)}
+`}function kz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Tl(e,h),f=["b","row","col"];return`
+        ${Cb(p)}
         vec4 ${r}(int b, int row, int col) {
           return ${r}(${Cl(f,d)});
         }
-      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=hn();return`
+      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=hn();return`
     vec4 ${r}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
-        ${i}, ${o}, ${c}, ${l}, b, row, col);
-      return ${u.texture2D}(${n}, uv);
+        ${i}, ${o}, ${u}, ${l}, b, row, col);
+      return ${c.texture2D}(${n}, uv);
     }
-  `}function gz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Tl(e,l),m=["row","col","depth"];return`
+  `}function yz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Tl(e,l),m=["row","col","depth"];return`
         ${Nl(f)}
         float ${r}(int row, int col, int depth) {
           return ${r}(${Cl(m,o)});
@@ -625,29 +625,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                           vec3(${a}, ${s}, 1)));
         ${Sl(e)}
       }
-    `;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
+    `;let u=e.shapeInfo.texShape,c=u[0],h=u[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
         float ${r}(int row, int col, int depth) {
           float texR = float(row);
           float texC = dot(vec2(col, depth), vec2(${s}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${h}.0, ${u}.0);
+                     vec2(${h}.0, ${c}.0);
           return sampleTexture(${n}, uv);
         }
       `;if(h===s&&d==null)return`
     float ${r}(int row, int col, int depth) {
       float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
       float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${c}.0);
       return sampleTexture(${n}, uv);
     }
   `;let p=bi(n);return`
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${a} + col * ${s} + depth + ${p};
-        vec2 uv = uvFromFlat(${u}, ${h}, index);
+        vec2 uv = uvFromFlat(${c}, ${h}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function Nz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=hn();return`
+  `}function Iz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,c*=t[n-f-1],d=`b${f} * ${c} + `+d;let p=hn();return`
     vec4 ${a}(${h}) {
       int index = ${d};
       int texR = index / ${l};
@@ -655,7 +655,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${p.texture2D}(${r}, uv);
     }
-  `}function xz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Tl(e,o),m=["row","col","depth","depth2"];return`
+  `}function gz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Tl(e,o),m=["row","col","depth","depth2"];return`
       ${Nl(f)}
       float ${r}(int row, int col, int depth, int depth2) {
         return ${r}(${Cl(m,l)});
@@ -666,7 +666,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                           vec4(${i}, ${s}, ${a}, 1)));
         ${Sl(e)}
       }
-    `;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
+    `;let u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],d=c[1];if(d===i&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
         float texR = float(row);
         float texC =
@@ -676,7 +676,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                    vec2(${d}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(d===a&&c==null)return`
+    `;if(d===a&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
         float texR = dot(vec3(row, col, depth),
                          vec3(${t[1]*t[2]}, ${t[2]}, 1));
@@ -693,10 +693,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
       return sampleTexture(${n}, uv);
     }
-  `}function wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=Tl(e,l),A=["row","col","depth","depth2","depth3"];return`
+  `}function xz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=Tl(e,l),A=["row","col","depth","depth2","depth3"];return`
       ${Nl(m)}
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${Cl(A,c)});
+        return ${r}(${Cl(A,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -706,7 +706,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           depth3;
         ${Sl(e)}
       }
-    `;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
+    `;let c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
@@ -715,7 +715,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                    vec2(${p}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(p===a&&u==null)return`
+    `;if(p===a&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
         float texR = dot(
           vec4(row, col, depth, depth2),
@@ -734,29 +734,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${p}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function bz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Tl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
+  `}function wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Tl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
       ${Nl(A)}
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         return ${r}(${Cl(y,s)});
       }
-    `}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
+    `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${u}, ${c}, ${l}, ${o})) +
+          vec4(${c}, ${u}, ${l}, ${o})) +
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
         ${Sl(e)}
       }
-    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
+    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===c&&h==null)return`
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${c}, ${l}, ${o}, ${i})) +
+          vec4(${u}, ${l}, ${o}, ${i})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${f}.0, ${p}.0);
@@ -779,7 +779,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${u} + col * ${c} + depth * ${l} +
+      int index = row * ${c} + col * ${u} + depth * ${l} +
           depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
       vec2 uv = uvFromFlat(${p}, ${f}, index);
       return sampleTexture(${n}, uv);
@@ -790,8 +790,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function Sz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Sb(e.shapeInfo.logicalShape,t.logicalShape),l=ot(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
-`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
+  `}function Nz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Tb(e.shapeInfo.logicalShape,t.logicalShape),l=ot(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(`
+`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?p=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
@@ -800,26 +800,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       `;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
     vec4 ${a}() {
       ${l} coords = getOutputCoords();
-      ${u}
+      ${c}
       vec4 outputValue = get${r}(${d});
       ${p}
     }
-  `}function Tz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
+  `}function Sz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let c=ot(l),u=Sb(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
+    `;let u=ot(l),c=Tb(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${p[m+h]} = 0;`).join(`
 `);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
     float ${a}() {
-      ${c} coords = getOutputCoords();
+      ${u} coords = getOutputCoords();
       ${d}
       return get${r}(${f});
     }
-  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Tl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Cl(e,t){return t.map(n=>e[n]).join(", ")}function Vz(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=fz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function Eb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Uz(e,t,n,r,a){Eb(t.inShapeInfos,n),Eb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function jz(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:Hz,bincountImpl:Rb,bincountReduceImpl:Gz,ceilImpl:qz,concatImpl:Xz,expImpl:Kz,expm1Impl:Zz,floorImpl:Yz,gatherV2Impl:Jz,greaterImpl:Qz,lessImpl:eP,linSpaceImpl:tP,logImpl:nP,maxImpl:rP,maximumImpl:aP,minimumImpl:sP,multiplyImpl:iP,negImpl:oP,prodImpl:lP,rangeImpl:uP,rsqrtImpl:cP,simpleAbsImpl:Mb,sliceImpl:hP,stridedSliceImpl:dP,subImpl:pP,tileImpl:fP,topKImpl:mP,transposeImpl:nA,uniqueImpl:AP}=Cm;function Fb(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function dn(e,t){return t===1?[e]:Fb(e,t)}function yP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var bP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
+  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Tl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Cl(e,t){return t.map(n=>e[n]).join(", ")}function Bz(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=pz(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(u,f,m),d[`offset${f}`]=e.getUniformLocation(u,`offset${f}`,m)}return{program:t,source:l,webGLProgram:u,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:c,nanLoc:h}}function Rb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Vz(e,t,n,r,a){Rb(t.inShapeInfos,n),Rb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],c=t.uniformLocations[u],h=t.uniformLocations[`offset${u}`];if(c!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(c,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(c,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,c,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function Uz(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:jz,bincountImpl:Mb,bincountReduceImpl:Hz,ceilImpl:Gz,concatImpl:qz,expImpl:Xz,expm1Impl:Kz,floorImpl:Zz,gatherV2Impl:Yz,greaterImpl:Jz,lessImpl:Qz,linSpaceImpl:eP,logImpl:tP,maxImpl:nP,maximumImpl:rP,minimumImpl:aP,multiplyImpl:sP,negImpl:iP,prodImpl:oP,rangeImpl:lP,rsqrtImpl:uP,simpleAbsImpl:Fb,sliceImpl:cP,stridedSliceImpl:hP,subImpl:dP,tileImpl:pP,topKImpl:fP,transposeImpl:aA,uniqueImpl:mP}=Rm;function $b(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function dn(e,t){return t===1?[e]:$b(e,t)}function AP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var wP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let n=dn("rc",t),r=ot(t),a=gP(t,e,n),s=xP(t,e[e.length-1],e[e.length-2],n),i=wP(e,n);this.userCode=`
+      `;else{let n=dn("rc",t),r=ot(t),a=yP(t,e,n),s=gP(t,e[e.length-1],e[e.length-2],n),i=xP(e,n);this.userCode=`
         void main() {
           ${r} rc = getOutputCoords();
 
@@ -831,7 +831,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function _P(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function gP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function xP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
+      `}}};function bP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function yP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function gP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
     int r = ${a[0]};
     int c = ${a[1]};
     int rp1 = r + 1;
@@ -839,12 +839,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function wP(e,t){let n=e.length,r=_P(n,t);return n===1?`getA(rc),
+  `}function xP(e,t){let n=e.length,r=bP(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${r[0]}),
           cEdge ? 0. : getA(${r[1]}),
           rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}var $b=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${r[3]})`}var Db=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
         ${a}
         ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${r>0?"}":""}
       `}this.userCode=`
-      ${vP(t)}
-      ${Zm(e)}
+      ${_P(t)}
+      ${Jm(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -872,12 +872,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function vP(e){return`
+    `}};function _P(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
       ${wi(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var kP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Ob(t,n),a=zb(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Db(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Qt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Qt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Qt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Ob(n,r),s=zb(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Db(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function IP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Db(e,t,n,r,a){let s=NP(t,r),i;if(a){let[l,c]=Il(e[0],e[1]);i=l*c}else{let[l,c]=cc(e[0],e[1]);i=l*c}let o=IP(n,s);return i*o}function NP(e,t){switch(e){case Qt.PACKED_2X2_FLOAT32:return eA(t);case Qt.PACKED_2X2_FLOAT16:return tA(t);case Qt.UNPACKED_FLOAT32:return Ym(t);case Qt.UNPACKED_FLOAT16:return Jm(t);case Qt.PACKED_4X1_UNSIGNED_BYTE:return Qm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Qt.PACKED_2X2_FLOAT32:Qt.UNPACKED_FLOAT32:e?Qt.PACKED_2X2_FLOAT16:Qt.UNPACKED_FLOAT16}function Ob(e,t){if(e===Xn.UPLOAD)return Qt.PACKED_2X2_FLOAT32;if(e===Xn.RENDER||e==null)return SP(t);if(e===Xn.DOWNLOAD||e===Xn.PIXELS)return Qt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function zb(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var La=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var vP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=zb(t,n),a=Pb(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Ob(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Qt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Qt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Qt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=zb(n,r),s=Pb(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Ob(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Ob(e,t,n,r,a){let s=IP(t,r),i;if(a){let[l,u]=Il(e[0],e[1]);i=l*u}else{let[l,u]=cc(e[0],e[1]);i=l*u}let o=kP(n,s);return i*o}function IP(e,t){switch(e){case Qt.PACKED_2X2_FLOAT32:return nA(t);case Qt.PACKED_2X2_FLOAT16:return rA(t);case Qt.UNPACKED_FLOAT32:return Qm(t);case Qt.UNPACKED_FLOAT16:return eA(t);case Qt.PACKED_4X1_UNSIGNED_BYTE:return tA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function NP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Qt.PACKED_2X2_FLOAT32:Qt.UNPACKED_FLOAT32:e?Qt.PACKED_2X2_FLOAT16:Qt.UNPACKED_FLOAT16}function zb(e,t){if(e===Xn.UPLOAD)return Qt.PACKED_2X2_FLOAT32;if(e===Xn.RENDER||e==null)return NP(t);if(e===Xn.DOWNLOAD||e===Xn.PIXELS)return Qt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Pb(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var La=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},gr="if (isnan(x)) return x;",TP="return x;",Pb="return abs(x);",CP="return (x >= 0.0) ? x : (exp(x) - 1.0);",EP=gr+`
+    `}},gr="if (isnan(x)) return x;",SP="return x;",Lb="return abs(x);",TP="return (x >= 0.0) ? x : (exp(x) - 1.0);",CP=gr+`
   return (x < 0.0) ? 0.0 : x;
-`,RP=gr+`
+`,EP=gr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,cp="return x;",MP="return x;",FP=`
+`,cp="return x;",RP="return x;",MP=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,$P=`
+`,FP=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,DP=`
+`,$P=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -932,14 +932,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},OP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=dn("rc",t),r=ot(t),a=yP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},DP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=dn("rc",t),r=ot(t),a=AP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${r} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},zP=Pr.whereImpl,PP=1e-7,LP=1e-4,rA={};function WP(e){return e in rA||(rA[e]={}),rA[e]}var BP=128,VP=600;function UP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*VP/1024/1024}var Rl=class extends uu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(J().getNumber("WEBGL_VERSION"));this.binaryCache=WP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new up(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new kP(this.gpgpu),this.numMBBeforeWarning=UP(),this.texData=new xh(this,Mr())}nextDataId(){return Rl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Xn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Xn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new El(i,cp):h=new La(i,cp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=R.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new El(r,cp):p=new La(r,cp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...hc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Mr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Lw(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...hc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?ap(t):t,o=s?new nz(i):new tz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Mr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=BP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return zP(e.shape,t)}packedUnaryOp(e,t,n){let r=new El(e.shape,t),a=this.compileAndRun(r,[e],n);return Mr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Mb(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Pb,e.dtype);let t=new La(e.shape,Pb),n=this.compileAndRun(t,[e]);return Mr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Mr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new OP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new bP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...gi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[yi(t),...gi(t)],s=new $b(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=ap(r),i;n?i=new ez(s):i=new QO(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===uc.DENSE){let m=hc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!lc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=jz(e,l,c),h=this.getAndSaveBinary(u,()=>Vz(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),Uz(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ge(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?PP:LP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=tb(n,o),t.texShape=u),a!=null){let h=ap(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=Il(u[0],u[1]),d=new az(h,[f,p],m)):d=new rz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Xn.PIXELS:this.texData.get(A.dataId).usage=Xn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=jP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Rl.nextDataId=0;function jP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var Lb="3.3.0";function Wb(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}zu.isBrowser()&&il("webgl",()=>new Rl,2);var HP={forceHalfFloat:Wb},Bb=`
+    `}},OP=Pr.whereImpl,zP=1e-7,PP=1e-4,sA={};function LP(e){return e in sA||(sA[e]={}),sA[e]}var WP=128,BP=600;function VP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*BP/1024/1024}var Rl=class extends uu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(J().getNumber("WEBGL_VERSION"));this.binaryCache=LP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new up(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new vP(this.gpgpu),this.numMBBeforeWarning=VP(),this.texData=new xh(this,Mr())}nextDataId(){return Rl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Xn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Xn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new El(i,cp):h=new La(i,cp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=R.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new El(r,cp):p=new La(r,cp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...hc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Mr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Ww(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...hc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?ap(t):t,o=s?new tz(i):new ez(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Mr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=WP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return OP(e.shape,t)}packedUnaryOp(e,t,n){let r=new El(e.shape,t),a=this.compileAndRun(r,[e],n);return Mr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Fb(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Lb,e.dtype);let t=new La(e.shape,Lb),n=this.compileAndRun(t,[e]);return Mr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Mr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new DP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new wP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...gi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[yi(t),...gi(t)],s=new Db(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=ap(r),i;n?i=new QO(s):i=new JO(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===uc.DENSE){let m=hc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!lc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=Uz(e,l,u),h=this.getAndSaveBinary(c,()=>Bz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),Vz(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ge(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zP:PP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=nb(n,o),t.texShape=c),a!=null){let h=ap(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=Il(c[0],c[1]),d=new rz(h,[f,p],m)):d=new nz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Xn.PIXELS:this.texData.get(A.dataId).usage=Xn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=UP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Rl.nextDataId=0;function UP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var Wb="3.3.0";function Bb(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}zu.isBrowser()&&il("webgl",()=>new Rl,2);var jP={forceHalfFloat:Bb},Vb=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,Ml=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
@@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function On(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var GP={kernelName:vs,backendName:"webgl",kernelFunc:On};function Wa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=On({inputs:{x:r},backend:n}),l=On({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var qP={kernelName:Th,backendName:"webgl",kernelFunc:Wa},Vb="return (a < 0.) ? b * a : a;",Ub=`
+    `}};function On(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var HP={kernelName:vs,backendName:"webgl",kernelFunc:On};function Wa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=On({inputs:{x:r},backend:n}),l=On({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var GP={kernelName:Th,backendName:"webgl",kernelFunc:Wa},Ub="return (a < 0.) ? b * a : a;",jb=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function XP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(Ub,a.shape,i.shape):new Ml(Vb,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var KP={kernelName:ks,backendName:"webgl",kernelFunc:XP},jb="return (a < 0.) ? b * a : a;",Hb=`
+`;function qP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(jb,a.shape,i.shape):new Ml(Ub,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var XP={kernelName:ks,backendName:"webgl",kernelFunc:qP},Hb="return (a < 0.) ? b * a : a;",Gb=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function ZP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(Hb,r.shape,a.shape):new Ml(jb,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var YP={kernelName:Os,backendName:"webgl",kernelFunc:ZP},Gb="if (isnan(x)) return x;",JP=`
+`;function KP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(Gb,r.shape,a.shape):new Ml(Hb,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var ZP={kernelName:Os,backendName:"webgl",kernelFunc:KP},qb="if (isnan(x)) return x;",YP=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,QP=`
+`,JP=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new El(i.shape,t):u=new La(i.shape,e),o.runWebGLProgram(u,[i],l)}}function en({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:c.shape},T=new Ml(e,l.shape,c.shape);return u.runWebGLProgram(T,[x,N],tr(b.dtype,_.dtype))}),g=Wa({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||tr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new pc(t,l.shape,c.shape,n):p=new Ml(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function dp(e,t=!1){if(e==="linear")return t?MP:TP;if(e==="relu")return t?$P:EP;if(e==="elu")return t?FP:CP;if(e==="relu6")return t?DP:RP;if(e==="prelu")return t?Hb:jb;if(e==="leakyrelu")return t?Ub:Vb;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var qb=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new El(i.shape,t):c=new La(i.shape,e),o.runWebGLProgram(c,[i],l)}}function en({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:u.shape},T=new Ml(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],tr(b.dtype,_.dtype))}),g=Wa({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||tr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),w=c.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new pc(t,l.shape,u.shape,n):p=new Ml(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function dp(e,t=!1){if(e==="linear")return t?RP:SP;if(e==="relu")return t?FP:CP;if(e==="elu")return t?MP:TP;if(e==="relu6")return t?$P:EP;if(e==="prelu")return t?Gb:Hb;if(e==="leakyrelu")return t?jb:Ub;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Xb=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1014,11 +1014,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${m}
 
-      const float sharedDimension = ${u}.0;
+      const float sharedDimension = ${c}.0;
 
       vec4 dot2x2ARowBCol(ivec3 rc) {
         vec4 result = vec4(0);
-        for (int i = 0; i < ${u}; i++) {
+        for (int i = 0; i < ${c}; i++) {
           int batchA = ${g};
           int batchB = ${w};
           vec4 a = getMatrixA(batchA, ${h});
@@ -1042,7 +1042,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},Xb={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Kb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},Kb={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Zb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},Zb="return a * b;";function Yb(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new Kb(Xb.REAL,r.shape,a.shape),u=new Kb(Xb.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=iP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new pc(Zb,r.shape,a.shape):i=new Ml(Zb,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var eL={kernelName:Ms,backendName:"webgl",kernelFunc:Yb};function tL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new $b(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),c=v.sizeFromShape(l);v.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!lc(a.shape,l)&&!(u.texture!==null&&lc(u.shape,l))?tL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var nL={kernelName:Oo,backendName:"webgl",kernelFunc:xe},Jb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
+    `}},Yb="return a * b;";function Jb(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new Zb(Kb.REAL,r.shape,a.shape),c=new Zb(Kb.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=sP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new pc(Yb,r.shape,a.shape):i=new Ml(Yb,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var QP={kernelName:Ms,backendName:"webgl",kernelFunc:Jb};function eL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new Db(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!lc(a.shape,l)&&!(c.texture!==null&&lc(c.shape,l))?eL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var tL={kernelName:Oo,backendName:"webgl",kernelFunc:xe},Qb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1063,7 +1063,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${c}
+        ${u}
         return getX(batch, inIdx);
       }
 
@@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},rL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
+    `}},nL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1150,7 +1150,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float allValue = 1.0;
         float anyValue = 0.0;
 
-        for (int i = 0; i < ${c}; i += 4) {
+        for (int i = 0; i < ${u}; i += 4) {
           int inIdx = inOffset + i;
           ${d} values = ${d}(
             getValue(batch, inIdx),
@@ -1162,8 +1162,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${h}
         }
 
-        int inIdx = inOffset + ${c};
-        if (${u===1}) {
+        int inIdx = inOffset + ${u};
+        if (${c===1}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             initializationValue,
@@ -1172,7 +1172,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           );
 
           ${h}
-        } else if (${u===2}) {
+        } else if (${c===2}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1181,7 +1181,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           );
 
           ${h}
-        } else if (${u===3}) {
+        } else if (${c===3}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function aL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function _i(e,t,n,r){let a=aL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new Jb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new Jb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new rL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var iL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=sL(t);this.userCode=`
+    `}};function rL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function _i(e,t,n,r){let a=rL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],c,h;n==="mean"?c=i===0?new Qb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Qb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):c=new nL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=s,s=r.runWebGLProgram(c,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var sL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=aL(t);this.userCode=`
     void main() {
       ${r} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function sL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var oL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ot(this.rank),a=Fb("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function aL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var iL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ot(this.rank),a=$b("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${r} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1215,14 +1215,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function pp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oL(e.shape,t):new iL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function lL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=pp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=sd(e.dtype),g=_i(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function aA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return lL(a,s,i,n)}var uL={kernelName:Gs,backendName:"webgl",kernelFunc:aA};function In(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=nA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=pp(a,s,i);return c}var cL={kernelName:Ys,backendName:"webgl",kernelFunc:In},Qb=1e3;function fp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),C=[N,T],F=Math.max(y,g),P=n?N.shape[1]:N.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",H=l!=null?dp(l,!0):null,X=W||V||U||H!=null,G;if((p===1||f===1)&&P>Qb&&X===!1){let Y=N,ae=T;n&&(Y=In({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),C.push(Y)),r&&(ae=In({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),C.push(ae));let te=f!==1,oe=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[F,P,1]}}),C.push(Q));let he=f===1?2:1,le=ae;oe&&(le=xe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,P]}}),C.push(le));let fe=Yb({inputs:{a:Q,b:le},backend:a});G=aA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),C.push(fe)}else{let Y=tr(e.dtype,t.dtype),ae=new qb(_,x,[F,p,f],n,r,W,H,V,U),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),U){let oe=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(oe),C.push(oe)}G=a.runWebGLProgram(ae,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});C.push(G);for(let Y of C)a.disposeIntermediateTensorInfo(Y);return ee}function hL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return fp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var dL={kernelName:Js,backendName:"webgl",kernelFunc:hL},e_="return abs(x);";function pL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Mb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,e_):a=new La(r.shape,e_),n.runWebGLProgram(a,[r],r.dtype)}var fL={kernelName:Yi,backendName:"webgl",kernelFunc:pL},mL=gr+`
+    `}};function pp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iL(e.shape,t):new sL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function oL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=pp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:c},attrs:{shape:[m,f]},backend:r}),y=sd(e.dtype),g=_i(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),w}function iA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return oL(a,s,i,n)}var lL={kernelName:Gs,backendName:"webgl",kernelFunc:iA};function In(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c<l.length;c++)l[c]=a.shape[s[c]];let u;if(i.shouldExecuteOnCPU([a])){let c=i.texData.get(a.dataId).values,h=aA(c,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(u.dataId);d.values=h}else u=pp(a,s,i);return u}var uL={kernelName:Ys,backendName:"webgl",kernelFunc:In},e_=1e3;function fp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),C=[N,T],F=Math.max(y,g),P=n?N.shape[1]:N.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",j=l!=null?dp(l,!0):null,X=W||V||U||j!=null,G;if((p===1||f===1)&&P>e_&&X===!1){let Y=N,ae=T;n&&(Y=In({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),C.push(Y)),r&&(ae=In({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),C.push(ae));let te=f!==1,oe=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[F,P,1]}}),C.push(Q));let he=f===1?2:1,le=ae;oe&&(le=xe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,P]}}),C.push(le));let fe=Jb({inputs:{a:Q,b:le},backend:a});G=iA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),C.push(fe)}else{let Y=tr(e.dtype,t.dtype),ae=new Xb(_,x,[F,p,f],n,r,W,j,V,U),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),U){let oe=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(oe),C.push(oe)}G=a.runWebGLProgram(ae,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});C.push(G);for(let Y of C)a.disposeIntermediateTensorInfo(Y);return ee}function cL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return fp({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var hL={kernelName:Js,backendName:"webgl",kernelFunc:cL},t_="return abs(x);";function dL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Fb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,t_):a=new La(r.shape,t_),n.runWebGLProgram(a,[r],r.dtype)}var pL={kernelName:Yi,backendName:"webgl",kernelFunc:dL},fL=gr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,AL=Xe({opSnippet:mL}),yL={kernelName:Ji,backendName:"webgl",kernelFunc:AL},gL=gr+`
+`,mL=Xe({opSnippet:fL}),AL={kernelName:Ji,backendName:"webgl",kernelFunc:mL},yL=gr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,backendName:"webgl",kernelFunc:xL},t_="return a + b;",bL=en({opSnippet:t_,packedOpSnippet:t_,supportsComplex:!0,cpuKernelImpl:Hz}),_L={kernelName:va,backendName:"webgl",kernelFunc:bL},vL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,gL=Xe({opSnippet:yL}),xL={kernelName:Qi,backendName:"webgl",kernelFunc:gL},n_="return a + b;",wL=en({opSnippet:n_,packedOpSnippet:n_,supportsComplex:!0,cpuKernelImpl:jz}),bL={kernelName:va,backendName:"webgl",kernelFunc:wL},_L=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,back
         float result = ${r};
         setOutput(result);
       }
-    `}},kL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},vL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,back
         vec4 result = ${r};
         setOutput(result);
       }
-    `}};function mp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return On({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=mp({inputs:r.slice(0,o),backend:n}),c=mp({inputs:r.slice(o),backend:n});return mp({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>tr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new kL(r[0].shape,s):new vL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var IL={kernelName:is,backendName:"webgl",kernelFunc:mp};function NL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("all",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var SL={kernelName:vh,backendName:"webgl",kernelFunc:NL};function TL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("any",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var CL={kernelName:kh,backendName:"webgl",kernelFunc:TL},EL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function mp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return On({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=mp({inputs:r.slice(0,o),backend:n}),u=mp({inputs:r.slice(o),backend:n});return mp({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>tr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new vL(r[0].shape,s):new _L(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var kL={kernelName:is,backendName:"webgl",kernelFunc:mp};function IL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var NL={kernelName:vh,backendName:"webgl",kernelFunc:IL};function SL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var TL={kernelName:kh,backendName:"webgl",kernelFunc:SL},CL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1258,23 +1258,23 @@ return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,back
         }
         setOutput(float(bestIndex));
       }
-    `}},RL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),c=dn("coords",o),u,h;if(s===1){h=o+1;let N=ot(h);u=`
-        ${N} sourceLocR = ${N}(${c.join()}, 0);
-        ++${c[o-1]};
-        ${N} sourceLocG = ${N}(${c.join()}, 0);
-        ++${c[o-2]};
-        ${N} sourceLocA = ${N}(${c.join()}, 0);
-        --${c[o-1]};
-        ${N} sourceLocB = ${N}(${c.join()}, 0);
-        --${c[o-2]};`}else h=o,u=`
+    `}},EL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),u=dn("coords",o),c,h;if(s===1){h=o+1;let N=ot(h);c=`
+        ${N} sourceLocR = ${N}(${u.join()}, 0);
+        ++${u[o-1]};
+        ${N} sourceLocG = ${N}(${u.join()}, 0);
+        ++${u[o-2]};
+        ${N} sourceLocA = ${N}(${u.join()}, 0);
+        --${u[o-1]};
+        ${N} sourceLocB = ${N}(${u.join()}, 0);
+        --${u[o-2]};`}else h=o,c=`
         ${l} sourceLocR = coords;
-        ++${c[o-1]};
+        ++${u[o-1]};
         ${l} sourceLocG = coords;
-        ++${c[o-2]};
+        ++${u[o-2]};
         ${l} sourceLocA = coords;
-        --${c[o-1]};
+        --${u[o-1]};
         ${l} sourceLocB = coords;
-        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=dn("sourceLocR",h-1).concat("inIdx.r"),A=dn("sourceLocG",h-1).concat("inIdx.g"),y=dn("sourceLocB",h-1).concat("inIdx.b"),g=dn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
+        --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=dn("sourceLocR",h-1).concat("inIdx.r"),A=dn("sourceLocG",h-1).concat("inIdx.g"),y=dn("sourceLocB",h-1).concat("inIdx.b"),g=dn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                              getBestIndicesAChannel(${A.join()}),
                              getBestIndicesAChannel(${y.join()}),
@@ -1294,9 +1294,9 @@ return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,back
       ${x}
       void main() {
         ${l} coords = getOutputCoords();
-        bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
-        bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
-        ${u}
+        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
+        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
+        ${c}
         ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
           sourceLocB${p}, sourceLocA${p}) * ${t};
         ivec4 inIdx = srcIdx;
@@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,back
         }
         setOutput(bestIndex);
       }
-    `}};function n_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new EL(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=n_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function r_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new RL(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=r_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function a_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=n_(e,c,r);s.push(u);let h=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return r_(e,t,r)}function ML(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=a_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var FL={kernelName:os,backendName:"webgl",kernelFunc:ML};function $L(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=a_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var DL={kernelName:du,backendName:"webgl",kernelFunc:$L},OL=gr+`
+    `}};function r_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new CL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=r_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function a_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new EL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=a_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function s_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=r_(e,u,r);s.push(c);let h=xe({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return a_(e,t,r)}function RL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=s_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var ML={kernelName:os,backendName:"webgl",kernelFunc:RL};function FL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=s_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var $L={kernelName:du,backendName:"webgl",kernelFunc:FL},DL=gr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,zL=Xe({opSnippet:OL}),PL={kernelName:eo,backendName:"webgl",kernelFunc:zL},LL=gr+"return log(x + sqrt(x * x + 1.0));",WL=Xe({opSnippet:LL}),BL={kernelName:to,backendName:"webgl",kernelFunc:WL},VL=gr+`
+`,OL=Xe({opSnippet:DL}),zL={kernelName:eo,backendName:"webgl",kernelFunc:OL},PL=gr+"return log(x + sqrt(x * x + 1.0));",LL=Xe({opSnippet:PL}),WL={kernelName:to,backendName:"webgl",kernelFunc:LL},BL=gr+`
   return atan(x);
-`,UL=Xe({opSnippet:VL}),jL={kernelName:no,backendName:"webgl",kernelFunc:UL},HL=JP+`
+`,VL=Xe({opSnippet:BL}),UL={kernelName:no,backendName:"webgl",kernelFunc:VL},jL=YP+`
   return atan(a, b);
-`,GL=`
+`,HL=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+QP+`
+  `+JP+`
   return result;
-`,qL=en({opSnippet:HL,packedOpSnippet:GL}),XL={kernelName:ao,backendName:"webgl",kernelFunc:qL},KL=gr+`
+`,GL=en({opSnippet:jL,packedOpSnippet:HL}),qL={kernelName:ao,backendName:"webgl",kernelFunc:GL},XL=gr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelName:ro,backendName:"webgl",kernelFunc:ZL},fc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,KL=Xe({opSnippet:XL}),ZL={kernelName:ro,backendName:"webgl",kernelFunc:KL},fc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${p});
 
@@ -1356,7 +1356,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           int minMaxPosition = 0;
           float avgValue = 0.0;
 
-          for (int wR = 0; wR < ${u};
+          for (int wR = 0; wR < ${c};
               wR += ${l}) {
             int xR = xRCorner + wR;
 
@@ -1365,7 +1365,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
             }
 
             for (int wC = 0; wC < ${h};
-                wC += ${c}) {
+                wC += ${u}) {
               int xC = xCCorner + wC;
 
               if (xC < 0 || xC >= ${e.inWidth}) {
@@ -1424,7 +1424,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wR = 0; wR < ${u};
+        for (int wR = 0; wR < ${c};
             wR += ${l}) {
           int xR = xRCorner + wR;
 
@@ -1433,13 +1433,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           }
 
           for (int wC = 0; wC < ${b}; wC += 4) {
-            int xC = xCCorner + wC * ${c};
+            int xC = xCCorner + wC * ${u};
 
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${c}, d),
-              getValue(batch, xR, xC + 2 * ${c}, d),
-              getValue(batch, xR, xC + 3 * ${c}, d)
+              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + 2 * ${u}, d),
+              getValue(batch, xR, xC + 3 * ${u}, d)
             );
 
             ${x}
@@ -1458,7 +1458,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           } else if (${_===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${c}, d),
+              getValue(batch, xR, xC + ${u}, d),
               initializationValue,
               initializationValue
             );
@@ -1467,8 +1467,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           } else if (${_===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${c}, d),
-              getValue(batch, xR, xC + 2 * ${c}, d),
+              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + 2 * ${u}, d),
               initializationValue
             );
 
@@ -1477,7 +1477,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(${w});
       }
-    `}},sA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
+    `}},oA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${m}, ${A}, ${y});
@@ -1499,7 +1499,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           int minMaxPosition = 0;
 
           for (int wD = 0; wD < ${d};
-              wD += ${c}) {
+              wD += ${u}) {
             int xD = xDCorner + wD;
 
             if (xD < 0 || xD >= ${e.inDepth}) {
@@ -1507,7 +1507,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
             }
 
             for (int wR = 0; wR < ${p};
-                wR += ${u}) {
+                wR += ${c}) {
               int xR = xRCorner + wR;
 
               if (xR < 0 || xR >= ${e.inHeight}) {
@@ -1579,7 +1579,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         count = 0.0;
 
         for (int wD = 0; wD < ${d};
-            wD += ${c}) {
+            wD += ${u}) {
           int xD = xDCorner + wD;
 
           if (xD < 0 || xD >= ${e.inDepth}) {
@@ -1587,7 +1587,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           }
 
           for (int wR = 0; wR < ${p};
-            wR += ${u}) {
+            wR += ${c}) {
             int xR = xRCorner + wR;
 
             if (xR < 0 || xR >= ${e.inHeight}) {
@@ -1640,8 +1640,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           setOutput(${_});
         }
       }
-    `}};function JL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return On({inputs:{x:a},backend:n});let h=new fc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var QL={kernelName:ls,backendName:"webgl",kernelFunc:JL};function eW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new sA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var tW={kernelName:pu,backendName:"webgl",kernelFunc:eW},nW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
-      const ivec2 pads = ivec2(${c}, ${u});
+    `}};function YL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return On({inputs:{x:a},backend:n});let h=new fc(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var JL={kernelName:ls,backendName:"webgl",kernelFunc:YL};function QL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new oA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var eW={kernelName:pu,backendName:"webgl",kernelFunc:QL},tW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
+      const ivec2 pads = ivec2(${u}, ${c});
       const float avgMultiplier = float(${h});
 
       void main() {
@@ -1682,7 +1682,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},rW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
+    `}},nW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
       const ivec3 pads = ivec3(${p}, ${f}, ${m});
       const float avgMultiplier = float(${A});
 
@@ -1701,7 +1701,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${u};
+        for (int wD = 0; wD < ${c};
             wD += ${o}) {
           float dyD = float(dyDCorner + wD) / ${a}.0;
 
@@ -1721,7 +1721,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
             int idyR = int(dyR);
 
             for (int wC = 0; wC < ${d};
-                wC += ${c}) {
+                wC += ${u}) {
               float dyC = float(dyCCorner + wC) / ${i}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function aW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new rW(d);return n.runWebGLProgram(p,[a],i.dtype)}var sW={kernelName:Nh,backendName:"webgl",kernelFunc:aW};function iW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;kl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=new nW(u);return n.runWebGLProgram(h,[a],i.dtype)}var oW={kernelName:Ih,backendName:"webgl",kernelFunc:iW};function lW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return fp({a,b:s,transposeA:i,transposeB:o,backend:n})}var uW={kernelName:us,backendName:"webgl",kernelFunc:lW},cW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function rW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new nW(d);return n.runWebGLProgram(p,[a],i.dtype)}var aW={kernelName:Nh,backendName:"webgl",kernelFunc:rW};function sW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;kl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=new tW(c);return n.runWebGLProgram(h,[a],i.dtype)}var iW={kernelName:Ih,backendName:"webgl",kernelFunc:sW};function oW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return fp({a,b:s,transposeA:i,transposeB:o,backend:n})}var lW={kernelName:us,backendName:"webgl",kernelFunc:oW},uW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},hW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},cW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},dW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new hW(r.shape,a.shape,s.shape,u,h,l):new cW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},pW={kernelName:bs,backendName:"webgl",kernelFunc:dW},mW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=fW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${iA[o]} = start[${o}] + coords.${iA[o]};`);a=`
+    `}},hW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new cW(r.shape,a.shape,s.shape,c,h,l):new uW(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},dW={kernelName:bs,backendName:"webgl",kernelFunc:hW},fW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=pW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${lA[o]} = start[${o}] + coords.${lA[o]};`);a=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${a}
         setOutput(getSource(${r}));
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},iA=["x","y","z","w","u","v"];function fW(e){if(e===1)return"sourceLoc";if(e<=6)return iA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var AW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=dn("coords",this.rank),r=dn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},lA=["x","y","z","w","u","v"];function pW(e){if(e===1)return"sourceLoc";if(e<=6)return lA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var mW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=dn("coords",this.rank),r=dn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${r[this.rank-1]};
@@ -1790,7 +1790,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
       }
     `,l=this.rank<=4?`sourceLoc = coords +
-            ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
+            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
 `);this.userCode=`
       uniform int start[${this.rank}];
       void main() {
@@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${o}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function yW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=ln.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function mc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=ln.parseSliceParams(a,s,i);if(ln.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=hP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=ln.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AW(l):new mW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),yW(a,o,l,n)}var gW={kernelName:Wo,backendName:"webgl",kernelFunc:mc},xW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=In({inputs:{x:f},backend:n,attrs:{perm:c}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=mc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},wW={kernelName:fu,backendName:"webgl",kernelFunc:xW};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=Rb(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var _W={kernelName:Sh,backendName:"webgl",kernelFunc:bW},vW="return float(a != b);",s_=en({opSnippet:vW,dtype:"bool"}),kW={kernelName:To,backendName:"webgl",kernelFunc:s_};function Ac(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.real},backend:n})}var IW={kernelName:Xh,backendName:"webgl",kernelFunc:Ac},NW="return float(int(x));";function SW(e,t){let n=new La(e.shape,NW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function oA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return On({inputs:{x:a},backend:n});let i=Ct(a.shape),o=oA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Wa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Ac({inputs:{input:a},backend:n}),o=oA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=On({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return SW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=s_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var TW={kernelName:cs,backendName:"webgl",kernelFunc:oA},i_="return ceil(x);",CW=Xe({opSnippet:i_,packedOpSnippet:i_,cpuKernelImpl:qz}),EW={kernelName:hs,backendName:"webgl",kernelFunc:CW},RW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function AW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=ln.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function mc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=ln.parseSliceParams(a,s,i);if(ln.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=cP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=ln.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mW(l):new fW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),AW(a,o,l,n)}var yW={kernelName:Wo,backendName:"webgl",kernelFunc:mc},gW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=In({inputs:{x:f},backend:n,attrs:{perm:u}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:c}}),y=mc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},xW={kernelName:fu,backendName:"webgl",kernelFunc:gW};function wW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Mb(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var bW={kernelName:Sh,backendName:"webgl",kernelFunc:wW},_W="return float(a != b);",i_=en({opSnippet:_W,dtype:"bool"}),vW={kernelName:To,backendName:"webgl",kernelFunc:i_};function Ac(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.real},backend:n})}var kW={kernelName:Xh,backendName:"webgl",kernelFunc:Ac},IW="return float(int(x));";function NW(e,t){let n=new La(e.shape,IW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function uA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return On({inputs:{x:a},backend:n});let i=Ct(a.shape),o=uA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Wa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Ac({inputs:{input:a},backend:n}),o=uA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=On({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return NW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=i_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var SW={kernelName:cs,backendName:"webgl",kernelFunc:uA},o_="return ceil(x);",TW=Xe({opSnippet:o_,packedOpSnippet:o_,cpuKernelImpl:Gz}),CW={kernelName:hs,backendName:"webgl",kernelFunc:TW},EW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},MW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},RW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function FW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new MW(a.shape):o=new RW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var $W={kernelName:ka,backendName:"webgl",kernelFunc:FW},DW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function MW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new RW(a.shape):o=new EW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var FW={kernelName:ka,backendName:"webgl",kernelFunc:MW},$W=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function o_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function OW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new DW(r.shape),i=[o_(r,a.complexTensorInfos.real),o_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var zW={kernelName:mu,backendName:"webgl",kernelFunc:OW},PW=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
+    `}};function l_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function DW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new $W(r.shape),i=[l_(r,a.complexTensorInfos.real),l_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var OW={kernelName:mu,backendName:"webgl",kernelFunc:DW},zW=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1851,18 +1851,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${n.join(`
         `)}
       }
-    `}},LW=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ot(r),s=dn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},PW=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ot(r),s=dn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),c=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
-            getT0(${u}), vec2(${c.join()}));
+            getT0(${c}), vec2(${u.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
             getT${f}(${Ap(i,l,m)}),
-            vec2(${Ap(c,l,m)}));
+            vec2(${Ap(u,l,m)}));
         }`}let d=o.length,p=o[o.length-1];h+=`
         return getChannel(
           getT${d}(${Ap(i,l,p)}),
-          vec2(${Ap(c,l,p)}));`,this.userCode=`
+          vec2(${Ap(u,l,p)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -1888,7 +1888,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(result);
       }
-    `}};function Ap(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function yp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.imag},backend:n})}var WW={kernelName:Bh,backendName:"webgl",kernelFunc:yp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>Ac({inputs:{input:f},backend:n})),u=e.map(f=>yp({inputs:{input:f},backend:n})),h=Fl(c,t,n),d=Fl(u,t,n),p=Wa({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=l_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=Xz(h,u,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Fl(e.slice(0,c),t,n),h=Fl(e.slice(c),t,n),d=Fl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new LW(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=l_(e,t,n),i=new PW(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function l_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function u_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(c=>c.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>v.sizeFromShape(c.shape)>0);if(o.length===1)return On({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return R.assertParamsConsistent(l,s),Fl(o,s,n)}var BW={kernelName:so,backendName:"webgl",kernelFunc:u_},c_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
+    `}};function Ap(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function yp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.imag},backend:n})}var LW={kernelName:Bh,backendName:"webgl",kernelFunc:yp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>Ac({inputs:{input:f},backend:n})),c=e.map(f=>yp({inputs:{input:f},backend:n})),h=Fl(u,t,n),d=Fl(c,t,n),p=Wa({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=u_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=qz(h,c,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=Fl(e.slice(0,u),t,n),h=Fl(e.slice(u),t,n),d=Fl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new PW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=u_(e,t,n),i=new zW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function u_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function c_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return On({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),Fl(o,s,n)}var WW={kernelName:so,backendName:"webgl",kernelFunc:c_},h_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:a?w=`float activation(float a) {
@@ -1918,14 +1918,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
         for (int wR = 0; wR < ${h}; wR++) {
-          int xR = xRCorner + wR * ${c};
+          int xR = xRCorner + wR * ${u};
 
           if (xR < 0 || xR >= ${e.inHeight}) {
             continue;
           }
 
           for (int wC = 0; wC < ${d}; wC++) {
-            int xC = xCCorner + wC * ${u};
+            int xC = xCCorner + wC * ${c};
 
             if (xC < 0 || xC >= ${e.inWidth}) {
               continue;
@@ -2022,7 +2022,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${b}
         setOutput(result);
       }
-    `}},VW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},BW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${r});
 
@@ -2040,7 +2040,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         // y(yF, yR, yC, d2). ? = to be determined. : = across all
         // values in that axis.
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${u}; wF++) {
+        for (int wF = 0; wF < ${c}; wF++) {
           int xF = xFCorner + wF * ${o};
 
           if (xF < 0 || xF >= ${e.inDepth}) {
@@ -2055,7 +2055,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
             }
 
             for (int wC = 0; wC < ${d}; wC++) {
-              int xC = xCCorner + wC * ${c};
+              int xC = xCCorner + wC * ${u};
 
               if (xC < 0 || xC >= ${e.inWidth}) {
                 continue;
@@ -2110,18 +2110,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},UW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=hn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
+    `}},VW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=hn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
           blockIndex = rc.y + ${_};
           pos = rc.x + ${b};
 
           if(blockIndex < ${e[1]} && pos < ${e[0]}) {
             offsetY = int(blockIndex / (${l})) * ${i} - ${p};
-            d0 = offsetY + ${u} * (pos / ${f});
+            d0 = offsetY + ${c} * (pos / ${f});
 
             if(d0 < ${t[y]} && d0 >= 0) {
 
               offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
-              d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
+              d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.));
 
               if(d1 < ${t[g]} && d1 >= 0) {
 
@@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
         ${m.output} = result;
       }
-    `}};function h_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>Qb,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=fp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(lc(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=fp({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,C.shape=n.outShape,A=On({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function d_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new UW(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),C=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(C);let F=a!=null,P=s!=null,W=o==="leakyrelu",V=o?dp(o,!0):null,U=new qb(C.shape,x.shape,[1,A,n.outChannels],g,w,F,V,P,W),H=[C,x];if(a&&H.push(a),P&&H.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function jW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=h_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=d_({x:a,filter:s,convInfo:d,backend:n});else{let m=new c_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var HW={kernelName:ds,backendName:"webgl",kernelFunc:jW},GW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function d_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>e_,w=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=fp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(lc(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=fp({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,C.shape=n.outShape,A=On({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function p_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new VW(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),C=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(C);let F=a!=null,P=s!=null,W=o==="leakyrelu",V=o?dp(o,!0):null,U=new Xb(C.shape,x.shape,[1,A,n.outChannels],g,w,F,V,P,W),j=[C,x];if(a&&j.push(a),P&&j.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,j,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function UW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=d_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=p_({x:a,filter:s,convInfo:d,backend:n});else{let m=new h_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var jW={kernelName:ds,backendName:"webgl",kernelFunc:UW},HW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2196,15 +2196,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},qW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
+    `}},GW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d1 = coords[${u}];
+        int d1 = coords[${c}];
 
-        ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
+        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
         int dyRCorner = dyCorner.x;
         int dyCCorner = dyCorner.y;
 
@@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},XW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},qW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2291,8 +2291,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},KW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${o}, ${l}, ${c});
+    `}},XW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${o}, ${l}, ${u});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function ZW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new GW(d);return n.runWebGLProgram(p,[a,s],"float32")}var YW={kernelName:Ch,backendName:"webgl",kernelFunc:ZW};function JW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(c),d=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new qW(d);return n.runWebGLProgram(p,[a,s],"float32")}var QW={kernelName:ps,backendName:"webgl",kernelFunc:JW};function eB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new VW(c);return n.runWebGLProgram(u,[a,s],"float32")}var tB={kernelName:Au,backendName:"webgl",kernelFunc:eB};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=R.computeConv3DInfo(a.shape,l,i,1,o),u=new XW(c);return n.runWebGLProgram(u,[a,s],"float32")}var rB={kernelName:Eh,backendName:"webgl",kernelFunc:nB};function aB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=R.computeConv3DInfo(l,s.shape,o,1,i),u=new KW(c);return n.runWebGLProgram(u,[a,s],"float32")}var sB={kernelName:Rh,backendName:"webgl",kernelFunc:aB},iB=Gb+`
+    `}};function KW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new HW(d);return n.runWebGLProgram(p,[a,s],"float32")}var ZW={kernelName:Ch,backendName:"webgl",kernelFunc:KW};function YW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(u),d=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new GW(d);return n.runWebGLProgram(p,[a,s],"float32")}var JW={kernelName:ps,backendName:"webgl",kernelFunc:YW};function QW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new BW(u);return n.runWebGLProgram(c,[a,s],"float32")}var eB={kernelName:Au,backendName:"webgl",kernelFunc:QW};function tB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=R.computeConv3DInfo(a.shape,l,i,1,o),c=new qW(u);return n.runWebGLProgram(c,[a,s],"float32")}var nB={kernelName:Eh,backendName:"webgl",kernelFunc:tB};function rB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=R.computeConv3DInfo(l,s.shape,o,1,i),c=new XW(u);return n.runWebGLProgram(c,[a,s],"float32")}var aB={kernelName:Rh,backendName:"webgl",kernelFunc:rB},sB=qb+`
   return cos(x);
-`,oB=Xe({opSnippet:iB}),lB={kernelName:fs,backendName:"webgl",kernelFunc:oB},uB=`
+`,iB=Xe({opSnippet:sB}),oB={kernelName:fs,backendName:"webgl",kernelFunc:iB},lB=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,cB=Xe({opSnippet:uB}),hB={kernelName:io,backendName:"webgl",kernelFunc:cB},dB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,uB=Xe({opSnippet:lB}),cB={kernelName:io,backendName:"webgl",kernelFunc:uB},hB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${g});
       void main() {
@@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           setOutput(newValue);
         }
       }
-    `}},pB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new dB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},fB={kernelName:oo,backendName:"webgl",kernelFunc:pB},m_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${p_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},dB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new hB(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},pB={kernelName:oo,backendName:"webgl",kernelFunc:dB},A_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${f_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
         ${ot(r)} coords = getOutputCoords();
-        int end = ${f_(r,"coords")};
+        int end = ${m_(r,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${f_(r,"coords")} = idx;
-          val += getX(${p_(r,"coords")});
+          ${m_(r,"coords")} = idx;
+          val += getX(${f_(r,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function p_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function f_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function mB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=R.getAxesPermutation([s],l),u=a;c!=null&&(u=In({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=On({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new m_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new m_(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=R.getUndoAxesPermutation(c),m=In({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var AB={kernelName:ms,backendName:"webgl",kernelFunc:mB};function yB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=Rb(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=Gz(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var gB={kernelName:Mh,backendName:"webgl",kernelFunc:yB},xB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function f_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function m_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function fB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=R.getAxesPermutation([s],l),c=a;u!=null&&(c=In({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=On({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new A_(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new A_(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=In({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var mB={kernelName:ms,backendName:"webgl",kernelFunc:fB};function AB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Mb(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=Hz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var yB={kernelName:Mh,backendName:"webgl",kernelFunc:AB},gB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2447,7 +2447,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function wB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new xB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var bB={kernelName:lo,backendName:"webgl",kernelFunc:wB},A_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new gB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var wB={kernelName:lo,backendName:"webgl",kernelFunc:xB},y_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:a?A=`float activation(float a) {
@@ -2460,7 +2460,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${A}
 
-      const ivec2 strides = ivec2(${c}, ${u});
+      const ivec2 strides = ivec2(${u}, ${c});
       const ivec2 pads = ivec2(${o}, ${l});
 
       void main() {
@@ -2503,13 +2503,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${y}
         setOutput(result);
       }
-    `}},y_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
+    `}},g_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
           vec4 xTexelR${b}C${_*2} = vec4(0.);
           vec4 wR${b}C${_} = vec4(0.);
           vec4 xR${b}C${_} = vec4(0.);`;for(let b=0;b<p;b++)for(let _=0;_<m;_++){let x=_*2;if(A+=`
           xR = xRCorner + ${b*h};
           xC = xCCorner + ${x*d};
-        `,u===1){if(x<f&&(l%2==1?A+=`
+        `,c===1){if(x<f&&(l%2==1?A+=`
                 xCOffset = xC + 1;
                 if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
@@ -2575,7 +2575,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
                 `}}else x<f&&(A+=`
               if(xR >= 0 && xR < ${s}) {
             `,l%2==1?(A+=`
-                xCOffset = xC + 1 - ${u};
+                xCOffset = xC + 1 - ${c};
                 if(xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
                 } else {
@@ -2592,7 +2592,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
                   xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
               `,x+1<f&&(A+=`
                   vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${u};
+                  xCOffset = xC + 1 + ${c};
                   if(xCOffset >= 0 && xCOffset < ${i}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
@@ -2604,7 +2604,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
                   xTexelR${b}C${x} = vec4(0.);
                 }
 
-                xCOffset = xC + ${u};
+                xCOffset = xC + ${c};
                 if(xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
                 } else {
@@ -2633,7 +2633,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }`,g="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${y}
 
-      const ivec2 strides = ivec2(${c}, ${u});
+      const ivec2 strides = ivec2(${u}, ${c});
       const ivec2 pads = ivec2(${o}, ${l});
 
       void main() {
@@ -2656,7 +2656,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${g}
         setOutput(result);
       }
-    `}};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new y_(h):d=new A_(h),n.runWebGLProgram(d,[a,s],"float32")}var vB={kernelName:As,backendName:"webgl",kernelFunc:_B},kB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new g_(h):d=new y_(h),n.runWebGLProgram(d,[a,s],"float32")}var _B={kernelName:As,backendName:"webgl",kernelFunc:bB},vB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},IB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},kB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2736,15 +2736,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function NB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new kB(h);return n.runWebGLProgram(d,[a,s],"float32")}var SB={kernelName:Fh,backendName:"webgl",kernelFunc:NB};function TB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new IB(h);return n.runWebGLProgram(d,[a,s],"float32")}var CB={kernelName:$h,backendName:"webgl",kernelFunc:TB},EB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function IB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new vB(h);return n.runWebGLProgram(d,[a,s],"float32")}var NB={kernelName:Fh,backendName:"webgl",kernelFunc:IB};function SB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new kB(h);return n.runWebGLProgram(d,[a,s],"float32")}var TB={kernelName:$h,backendName:"webgl",kernelFunc:SB},CB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function RB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new EB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var MB={kernelName:Dh,backendName:"webgl",kernelFunc:RB},FB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
+    `}};function EB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new CB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var RB={kernelName:Dh,backendName:"webgl",kernelFunc:EB},MB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
-      const ivec2 pads = ivec2(${u}, ${h});
+      const ivec2 pads = ivec2(${c}, ${h});
       const float neg_infinity = -3.4e38;
 
       void main() {
@@ -2762,7 +2762,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
           if (hIn >= 0 && hIn < ${t}) {
             for (int w = 0; w < ${o}; w++) {
-              int wIn = wBeg + w * ${c};
+              int wIn = wBeg + w * ${u};
 
               if (wIn >= 0 && wIn < ${n}) {
                 float xVal = getX(batch, hIn, wIn, d1);
@@ -2780,7 +2780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function $B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new FB(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var DB={kernelName:yu,backendName:"webgl",kernelFunc:$B},OB="return (x >= 0.0) ? x : (exp(x) - 1.0);",zB=`
+    `}};function FB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new MB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var $B={kernelName:yu,backendName:"webgl",kernelFunc:FB},DB="return (x >= 0.0) ? x : (exp(x) - 1.0);",OB=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2789,12 +2789,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,PB=Xe({opSnippet:OB,packedOpSnippet:zB}),LB={kernelName:uo,backendName:"webgl",kernelFunc:PB},WB="return (b >= 1.0) ? a : a * (b + 1.0);",BB=`
+`,zB=Xe({opSnippet:DB,packedOpSnippet:OB}),PB={kernelName:uo,backendName:"webgl",kernelFunc:zB},LB="return (b >= 1.0) ? a : a * (b + 1.0);",WB=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,VB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(BB,r.shape,a.shape):new Ml(WB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},UB={kernelName:Ph,backendName:"webgl",kernelFunc:VB},jB=`
+`,BB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(WB,r.shape,a.shape):new Ml(LB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},VB={kernelName:Ph,backendName:"webgl",kernelFunc:BB},UB=`
   return vec4(equal(a, b));
-`,HB="return float(a == b);",GB=en({opSnippet:HB,packedOpSnippet:jB,dtype:"bool"}),qB={kernelName:ho,backendName:"webgl",kernelFunc:GB},XB=`
+`,jB="return float(a == b);",HB=en({opSnippet:jB,packedOpSnippet:UB,dtype:"bool"}),GB={kernelName:ho,backendName:"webgl",kernelFunc:HB},qB=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -2809,7 +2809,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,KB=Xe({opSnippet:XB}),ZB={kernelName:co,backendName:"webgl",kernelFunc:KB},g_="return exp(x);",x_=Xe({opSnippet:g_,packedOpSnippet:g_,cpuKernelImpl:Kz}),YB={kernelName:gs,backendName:"webgl",kernelFunc:x_};function lA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var JB={kernelName:po,backendName:"webgl",kernelFunc:lA},w_="return exp(x) - 1.0;",QB=Xe({opSnippet:w_,packedOpSnippet:w_,cpuKernelImpl:Zz}),eV={kernelName:fo,backendName:"webgl",kernelFunc:QB},b_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,XB=Xe({opSnippet:qB}),KB={kernelName:co,backendName:"webgl",kernelFunc:XB},x_="return exp(x);",w_=Xe({opSnippet:x_,packedOpSnippet:x_,cpuKernelImpl:Xz}),ZB={kernelName:gs,backendName:"webgl",kernelFunc:w_};function cA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var YB={kernelName:po,backendName:"webgl",kernelFunc:cA},b_="return exp(x) - 1.0;",JB=Xe({opSnippet:b_,packedOpSnippet:b_,cpuKernelImpl:Kz}),QB={kernelName:fo,backendName:"webgl",kernelFunc:JB},__=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2842,13 +2842,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function __(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new b_("real",l,t),u=new b_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function tV(e){let{inputs:t,backend:n}=e,{input:r}=t;return __(r,!1,n)}var nV={kernelName:Lh,backendName:"webgl",kernelFunc:tV},rV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function v_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new __("real",l,t),c=new __("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function eV(e){let{inputs:t,backend:n}=e,{input:r}=t;return v_(r,!1,n)}var tV={kernelName:Lh,backendName:"webgl",kernelFunc:eV},nV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function uA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new rV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var aV={kernelName:gu,backendName:"webgl",kernelFunc:uA},sV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function hA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new nV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var rV={kernelName:gu,backendName:"webgl",kernelFunc:hA},aV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -2862,7 +2862,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},iV={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new sV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},v_="return floor(x);",oV=Xe({opSnippet:v_,packedOpSnippet:v_,cpuKernelImpl:Yz}),lV={kernelName:xs,backendName:"webgl",kernelFunc:oV},uV=`
+    `}},sV={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new aV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},k_="return floor(x);",iV=Xe({opSnippet:k_,packedOpSnippet:k_,cpuKernelImpl:Zz}),oV={kernelName:xs,backendName:"webgl",kernelFunc:iV},lV=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2872,7 +2872,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
   } else {
     return NAN;
   }
-`,cV=`
+`,uV=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2893,7 +2893,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,hV=en({opSnippet:uV,packedOpSnippet:cV,dtype:"int32"}),dV={kernelName:ws,backendName:"webgl",kernelFunc:hV},pV=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
+`,cV=en({opSnippet:lV,packedOpSnippet:uV,dtype:"int32"}),hV={kernelName:ws,backendName:"webgl",kernelFunc:cV},dV=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2915,7 +2915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},fV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
+    `}},pV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
 
         ${t.output} = result;
       }
-    `}},AV={kernelName:ed,backendName:"webgl",kernelFunc:mV},$l;function mV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&($l==null&&($l=document.createElement("canvas").getContext("2d")),$l.canvas.width=l,$l.canvas.height=c,$l.drawImage(a,0,0,l,c),a=$l.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=Xn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new fV(h):new pV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function yV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(u),A=R.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=h_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=d_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?dp(p,!1):null,T=new c_(A,b,N,_,x),C=[a,s];if(i&&C.push(i),o&&C.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(T,C,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var gV={kernelName:Qs,backendName:"webgl",kernelFunc:yV};function xV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?dp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let C=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(C),f.push(C)}let N;y?N=new y_(A,b,g,_,x):N=new A_(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(C=>n.disposeIntermediateTensorInfo(C)),T}var wV={kernelName:ei,backendName:"webgl",kernelFunc:xV},bV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ot(t.length),a=ot(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},mV={kernelName:ed,backendName:"webgl",kernelFunc:fV},$l;function fV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[u,l],h=[u,l,s];(o||i)&&($l==null&&($l=document.createElement("canvas").getContext("2d")),$l.canvas.width=l,$l.canvas.height=u,$l.drawImage(a,0,0,l,u),a=$l.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Xn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new pV(h):new dV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function AV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(c),A=R.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=d_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=p_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?dp(p,!1):null,T=new h_(A,b,N,_,x),C=[a,s];if(i&&C.push(i),o&&C.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(T,C,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var yV={kernelName:Qs,backendName:"webgl",kernelFunc:AV};function gV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?dp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let C=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(C),f.push(C)}let N;y?N=new g_(A,b,g,_,x):N=new y_(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(C=>n.disposeIntermediateTensorInfo(C)),T}var xV={kernelName:ei,backendName:"webgl",kernelFunc:gV},wV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ot(t.length),a=ot(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${r} strides = ${r}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -2960,21 +2960,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function _V(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/c,c]}}),p=new bV(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var vV={kernelName:yo,backendName:"webgl",kernelFunc:_V},IV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),r=kV(e,2);this.userCode=`
+      `}};function bV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new wV(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var _V={kernelName:yo,backendName:"webgl",kernelFunc:bV},kV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),r=vV(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function kV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function NV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=Jz(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new IV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var SV={kernelName:Ao,backendName:"webgl",kernelFunc:NV},TV="return float(a > b);",CV=`
+    `}};function vV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function IV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});h.push(d),h.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=Yz(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new kV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var NV={kernelName:Ao,backendName:"webgl",kernelFunc:IV},SV="return float(a > b);",TV=`
   return vec4(greaterThan(a, b));
-`,EV=en({opSnippet:TV,packedOpSnippet:CV,cpuKernelImpl:Qz,dtype:"bool"}),RV={kernelName:go,backendName:"webgl",kernelFunc:EV},MV="return float(a >= b);",FV=`
+`,CV=en({opSnippet:SV,packedOpSnippet:TV,cpuKernelImpl:Jz,dtype:"bool"}),EV={kernelName:go,backendName:"webgl",kernelFunc:CV},RV="return float(a >= b);",MV=`
   return vec4(greaterThanEqual(a, b));
-`,$V=en({opSnippet:MV,packedOpSnippet:FV,dtype:"bool"}),DV={kernelName:_s,backendName:"webgl",kernelFunc:$V};function OV(e){let{inputs:t,backend:n}=e,{input:r}=t;return __(r,!0,n)}var zV={kernelName:Wh,backendName:"webgl",kernelFunc:OV},PV="return float(!isnan(x) && !isinf(x));",LV=Xe({opSnippet:PV,dtype:"bool"}),WV={kernelName:xo,backendName:"webgl",kernelFunc:LV},BV="return float(isinf(x));",VV=Xe({opSnippet:BV,dtype:"bool"}),UV={kernelName:wo,backendName:"webgl",kernelFunc:VV},jV="return float(isnan(x));",HV=Xe({opSnippet:jV,dtype:"bool"}),GV={kernelName:bo,backendName:"webgl",kernelFunc:HV},qV="return float(a < b);",XV=`
+`,FV=en({opSnippet:RV,packedOpSnippet:MV,dtype:"bool"}),$V={kernelName:_s,backendName:"webgl",kernelFunc:FV};function DV(e){let{inputs:t,backend:n}=e,{input:r}=t;return v_(r,!0,n)}var OV={kernelName:Wh,backendName:"webgl",kernelFunc:DV},zV="return float(!isnan(x) && !isinf(x));",PV=Xe({opSnippet:zV,dtype:"bool"}),LV={kernelName:xo,backendName:"webgl",kernelFunc:PV},WV="return float(isinf(x));",BV=Xe({opSnippet:WV,dtype:"bool"}),VV={kernelName:wo,backendName:"webgl",kernelFunc:BV},UV="return float(isnan(x));",jV=Xe({opSnippet:UV,dtype:"bool"}),HV={kernelName:bo,backendName:"webgl",kernelFunc:jV},GV="return float(a < b);",qV=`
   return vec4(lessThan(a, b));
-`,KV=en({opSnippet:qV,packedOpSnippet:XV,cpuKernelImpl:eP,dtype:"bool"}),ZV={kernelName:_o,backendName:"webgl",kernelFunc:KV},YV="return float(a <= b);",JV=`
+`,XV=en({opSnippet:GV,packedOpSnippet:qV,cpuKernelImpl:Qz,dtype:"bool"}),KV={kernelName:_o,backendName:"webgl",kernelFunc:XV},ZV="return float(a <= b);",YV=`
   return vec4(lessThanEqual(a, b));
-`,QV=en({opSnippet:YV,packedOpSnippet:JV,dtype:"bool"}),eU={kernelName:vo,backendName:"webgl",kernelFunc:QV};function tU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=tP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var nU={kernelName:Vh,backendName:"webgl",kernelFunc:tU},rU=`if (x < 0.0) return NAN;
-  return log(x);`,aU=`
+`,JV=en({opSnippet:ZV,packedOpSnippet:YV,dtype:"bool"}),QV={kernelName:vo,backendName:"webgl",kernelFunc:JV};function eU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=eP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var tU={kernelName:Vh,backendName:"webgl",kernelFunc:eU},nU=`if (x < 0.0) return NAN;
+  return log(x);`,rU=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2983,16 +2983,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,sU=Xe({opSnippet:rU,packedOpSnippet:aU,cpuKernelImpl:nP}),iU={kernelName:Is,backendName:"webgl",kernelFunc:sU},oU="return log(1.0 + x);",lU=Xe({opSnippet:oU}),uU={kernelName:ko,backendName:"webgl",kernelFunc:lU},cU="return float(a >= 1.0 && b >= 1.0);",hU=`
+`,aU=Xe({opSnippet:nU,packedOpSnippet:rU,cpuKernelImpl:tP}),sU={kernelName:Is,backendName:"webgl",kernelFunc:aU},iU="return log(1.0 + x);",oU=Xe({opSnippet:iU}),lU={kernelName:ko,backendName:"webgl",kernelFunc:oU},uU="return float(a >= 1.0 && b >= 1.0);",cU=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,dU=en({opSnippet:cU,packedOpSnippet:hU,dtype:"bool"}),pU={kernelName:Io,backendName:"webgl",kernelFunc:dU},fU="return float(!(x >= 1.0));",mU=Xe({opSnippet:fU}),AU={kernelName:xu,backendName:"webgl",kernelFunc:mU},yU="return float(a >= 1.0 || b >= 1.0);",gU=`
+`,hU=en({opSnippet:uU,packedOpSnippet:cU,dtype:"bool"}),dU={kernelName:Io,backendName:"webgl",kernelFunc:hU},pU="return float(!(x >= 1.0));",fU=Xe({opSnippet:pU}),mU={kernelName:xu,backendName:"webgl",kernelFunc:fU},AU="return float(a >= 1.0 || b >= 1.0);",yU=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,xU=en({opSnippet:yU,packedOpSnippet:gU,dtype:"bool"}),wU={kernelName:wu,backendName:"webgl",kernelFunc:xU},bU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,gU=en({opSnippet:AU,packedOpSnippet:yU,dtype:"bool"}),xU={kernelName:wu,backendName:"webgl",kernelFunc:gU},wU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3011,7 +3011,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},_U=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},bU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3073,7 +3073,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},vU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new _U(a.shape,s,i,o,l):new bU(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},kU={kernelName:bu,backendName:"webgl",kernelFunc:vU},IU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
+    `}},_U=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new bU(a.shape,s,i,o,l):new wU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},vU={kernelName:bu,backendName:"webgl",kernelFunc:_U},kU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3128,14 +3128,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
       }
       setOutput(result);
       }
-    `}},NU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new IU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},SU={kernelName:Uh,backendName:"webgl",kernelFunc:NU};function TU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function k_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let b=nA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=pp(a,u,n);c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("max",c,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=rP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=TU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var CU={kernelName:Ns,backendName:"webgl",kernelFunc:k_},EU=Bb+`
+    `}},IU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new kU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},NU={kernelName:Uh,backendName:"webgl",kernelFunc:IU};function SU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function I_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[c[x]];let b=aA(g,a.shape,a.dtype,c,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=pp(a,c,n);u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("max",u,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,u),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=nP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=SU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var TU={kernelName:Ns,backendName:"webgl",kernelFunc:I_},CU=Vb+`
   return max(a, b);
-`,RU=`
+`,EU=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+hp+`
   return result;
-`,MU=en({opSnippet:EU,packedOpSnippet:RU,cpuKernelImpl:aP}),FU={kernelName:Ss,backendName:"webgl",kernelFunc:MU};function $U(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return On({inputs:{x:a},backend:n});let h=new fc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var DU={kernelName:Ts,backendName:"webgl",kernelFunc:$U};function OU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new sA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var zU={kernelName:_u,backendName:"webgl",kernelFunc:OU},PU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,RU=en({opSnippet:CU,packedOpSnippet:EU,cpuKernelImpl:rP}),MU={kernelName:Ss,backendName:"webgl",kernelFunc:RU};function FU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return On({inputs:{x:a},backend:n});let h=new fc(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var $U={kernelName:Ts,backendName:"webgl",kernelFunc:FU};function DU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new oA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var OU={kernelName:_u,backendName:"webgl",kernelFunc:DU},zU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3181,8 +3181,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},LU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
-      const ivec3 pads = ivec3(${u}, ${h}, ${d});
+    `}},PU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=`
+      const ivec3 pads = ivec3(${c}, ${h}, ${d});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -3218,7 +3218,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${c};
+            for (int wC = 0; wC < ${u};
                 wC += ${i}) {
               float dyC = float(dyCCorner + wC) / ${r}.0;
 
@@ -3235,8 +3235,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
               // Get the current value, check it against the value from the
               // position matrix.
               int curPosValue =
-                  wD * ${l} * ${c} +
-                  wR * ${c} + wC;
+                  wD * ${l} * ${u} +
+                  wR * ${u} + wC;
               float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
               dotProd += dyValue * mask;
@@ -3245,14 +3245,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function WU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new sA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new LU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var BU={kernelName:Hh,backendName:"webgl",kernelFunc:WU};function VU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;kl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new fc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new PU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var UU={kernelName:jh,backendName:"webgl",kernelFunc:VU};function jU(e,t,n,r){let a=new fc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new fc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var HU={kernelName:Gh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=R.computePool2DInfo(r.shape,a,s,c,i),[h,d]=jU(r,o,u,l);return[h,d]}};function GU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var qU={kernelName:Cs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[u[N]];let _=nA(w,r.shape,r.dtype,u,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=pp(r,u,i);p.push(f),c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=GU(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function XU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var KU={kernelName:Es,backendName:"webgl",kernelFunc:XU},ZU=Bb+`
+    `}};function LU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new oA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new PU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var WU={kernelName:Hh,backendName:"webgl",kernelFunc:LU};function BU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;kl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new fc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new zU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var VU={kernelName:jh,backendName:"webgl",kernelFunc:BU};function UU(e,t,n,r){let a=new fc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new fc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var jU={kernelName:Gh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,a,s,u,i),[h,d]=UU(r,o,c,l);return[h,d]}};function HU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var GU={kernelName:Cs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[c[N]];let _=aA(w,r.shape,r.dtype,c,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=pp(r,c,i);p.push(f),u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("sum",u,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=HU(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function qU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var XU={kernelName:Es,backendName:"webgl",kernelFunc:qU},KU=Vb+`
   return min(a, b);
-`,YU=`
+`,ZU=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+hp+`
   return result;
-`,JU=en({opSnippet:ZU,packedOpSnippet:YU,cpuKernelImpl:sP}),QU={kernelName:Rs,backendName:"webgl",kernelFunc:JU},ej=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ot(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
+`,YU=en({opSnippet:KU,packedOpSnippet:ZU,cpuKernelImpl:aP}),JU={kernelName:Rs,backendName:"webgl",kernelFunc:YU},QU=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=ot(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3281,7 +3281,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},tj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=dn("rc",r),l=dn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
+    `}},ej=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=dn("rc",r),l=dn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3292,11 +3292,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
       `;d=`
         ${a} rc = outputLoc;
         ${p}
-        result[0] = getChannel(getX(${l.join()}), ${u});
+        result[0] = getChannel(getX(${l.join()}), ${c});
         ${o[r-1]} += 1;
-        if(${c}) {
+        if(${u}) {
           ${p}
-          result[1] = getChannel(getX(${l.join()}), ${u});
+          result[1] = getChannel(getX(${l.join()}), ${c});
         }
       `}else{let p=`
         ${a} source = rc;
@@ -3310,21 +3310,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
       `;d=`
         ${a} rc = outputLoc;
         ${p}
-        result[0] = getChannel(getX(${l.join()}), ${u});
+        result[0] = getChannel(getX(${l.join()}), ${c});
         ${o[r-1]} += 1;
-        if(${c}) {
+        if(${u}) {
           ${p}
-          result[1] = getChannel(getX(${l.join()}), ${u});
+          result[1] = getChannel(getX(${l.join()}), ${c});
         }
         rc = outputLoc;
         ${o[r-2]} += 1;
         if(${o[r-2]} < ${this.outputShape[r-2]}) {
           ${p}
-          result[2] = getChannel(getX(${l.join()}), ${u});
+          result[2] = getChannel(getX(${l.join()}), ${c});
           ${o[r-1]} += 1;
-          if(${c}) {
+          if(${u}) {
             ${p}
-            result[3] = getChannel(getX(${l.join()}), ${u});
+            result[3] = getChannel(getX(${l.join()}), ${c});
           }
         }
       `}this.userCode=`
@@ -3337,13 +3337,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},nj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tj(r.shape,a,s):new ej(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},rj={kernelName:vu,backendName:"webgl",kernelFunc:nj},aj=`if (b == 0.0) return NAN;
-  return mod(a, b);`,sj=`
+    `}},tj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ej(r.shape,a,s):new QU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},nj={kernelName:vu,backendName:"webgl",kernelFunc:tj},rj=`if (b == 0.0) return NAN;
+  return mod(a, b);`,aj=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+hp+`
   return result;
-`,ij=en({opSnippet:aj,packedOpSnippet:sj}),oj={kernelName:No,backendName:"webgl",kernelFunc:ij},lj=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,sj=en({opSnippet:rj,packedOpSnippet:aj}),ij={kernelName:No,backendName:"webgl",kernelFunc:sj},oj=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3365,11 +3365,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},uj=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},lj=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,cj=`
+return a / b;`,uj=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3387,14 +3387,14 @@ return a / b;`,cj=`
   }
 
   return result;
-`,I_=en({opSnippet:uj,packedOpSnippet:cj,checkOutOfBounds:!0}),hj={kernelName:ys,backendName:"webgl",kernelFunc:I_},N_="return a - b;",S_=en({opSnippet:N_,packedOpSnippet:N_,supportsComplex:!0,cpuKernelImpl:pP}),dj={kernelName:Ks,backendName:"webgl",kernelFunc:S_};function T_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=k_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=S_({inputs:{a,b:c},backend:n}),h=x_({inputs:{x:u},backend:n}),d=aA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=I_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var pj={kernelName:qs,backendName:"webgl",kernelFunc:T_};function fj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:T_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new lj(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var mj={kernelName:qh,backendName:"webgl",kernelFunc:fj},C_="return -x;";function Aj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=oP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,C_):a=new La(r.shape,C_),n.runWebGLProgram(a,[r],r.dtype)}var yj={kernelName:So,backendName:"webgl",kernelFunc:Aj},gj=Pr.nonMaxSuppressionV3Impl;function xj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=gj(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var wj={kernelName:Co,backendName:"webgl",kernelFunc:xj},bj=Pr.nonMaxSuppressionV4Impl;function _j(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=bj(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var vj={kernelName:Eo,backendName:"webgl",kernelFunc:_j},kj=Pr.nonMaxSuppressionV5Impl;function Ij(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=kj(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Nj={kernelName:Ro,backendName:"webgl",kernelFunc:Ij},Sj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,N_=en({opSnippet:lj,packedOpSnippet:uj,checkOutOfBounds:!0}),cj={kernelName:ys,backendName:"webgl",kernelFunc:N_},S_="return a - b;",T_=en({opSnippet:S_,packedOpSnippet:S_,supportsComplex:!0,cpuKernelImpl:dP}),hj={kernelName:Ks,backendName:"webgl",kernelFunc:T_};function C_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=I_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),c=T_({inputs:{a,b:u},backend:n}),h=w_({inputs:{x:c},backend:n}),d=iA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=N_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var dj={kernelName:qs,backendName:"webgl",kernelFunc:C_};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:C_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new oj(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var fj={kernelName:qh,backendName:"webgl",kernelFunc:pj},E_="return -x;";function mj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=iP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,E_):a=new La(r.shape,E_),n.runWebGLProgram(a,[r],r.dtype)}var Aj={kernelName:So,backendName:"webgl",kernelFunc:mj},yj=Pr.nonMaxSuppressionV3Impl;function gj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=yj(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var xj={kernelName:Co,backendName:"webgl",kernelFunc:gj},wj=Pr.nonMaxSuppressionV4Impl;function bj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=wj(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var _j={kernelName:Eo,backendName:"webgl",kernelFunc:bj},vj=Pr.nonMaxSuppressionV5Impl;function kj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=vj(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Ij={kernelName:Ro,backendName:"webgl",kernelFunc:kj},Nj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${r}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},Tj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),c=new Sj(l,s,i,o),u=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},Cj={kernelName:Fs,backendName:"webgl",kernelFunc:Tj};function gp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=gp({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return uA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Ej={kernelName:Ko,backendName:"webgl",kernelFunc:gp};function E_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=E_({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return uA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Rj={kernelName:Mo,backendName:"webgl",kernelFunc:E_};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return lA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=lA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=u_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Fj={kernelName:Fo,backendName:"webgl",kernelFunc:Mj},$j=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ot(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
+    `}},Sj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new Nj(l,s,i,o),c=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},Tj={kernelName:Fs,backendName:"webgl",kernelFunc:Sj};function gp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=gp({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return hA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Cj={kernelName:Ko,backendName:"webgl",kernelFunc:gp};function R_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=R_({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return hA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Ej={kernelName:Mo,backendName:"webgl",kernelFunc:R_};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return cA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=cA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=c_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Mj={kernelName:Fo,backendName:"webgl",kernelFunc:Rj},Fj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=ot(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
         uniform float value;
@@ -3421,19 +3421,19 @@ return a / b;`,cj=`
           setOutput(getX(${o}));
         }
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},Dj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ot(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=dn("rc",r),l=dn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
-       if(${c}) {
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},$j=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ot(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=dn("rc",r),l=dn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
+       if(${u}) {
       `,r===1?"":`}
        rc = outputLoc;
        ${o[r-2]} += 1;
        if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":`  ${o[r-1]} += 1;
-         if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
+         if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
         ${h[f]}
         if (${d}) {
           result[${f}] = float(value);
         } else {
           ${a} source = rc - start;
-          result[${f}] = getChannel(getX(${l.join()}), ${u});
+          result[${f}] = getChannel(getX(${l.join()}), ${c});
         }
       `;p+=r===1?"} ":"}}",this.userCode=`
       const ${a} start = ${a}(${s});
@@ -3446,7 +3446,7 @@ return a / b;`,cj=`
         ${p}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},R_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dj(a.shape,s,i):new $j(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},Oj={kernelName:$s,backendName:"webgl",kernelFunc:R_},zj=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},M_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $j(a.shape,s,i):new Fj(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},Dj={kernelName:$s,backendName:"webgl",kernelFunc:M_},Oj=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3455,7 +3455,7 @@ return a / b;`,cj=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,Pj=`
+`,zj=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3471,9 +3471,9 @@ return a / b;`,cj=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+hp+`
   return result;
-`,Lj=en({opSnippet:zj,packedOpSnippet:Pj}),Wj={kernelName:Ds,backendName:"webgl",kernelFunc:Lj};function Bj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=v.parseAxisParam(s,a.shape),u=c,h=R.getAxesPermutation(u,o),d=a;h!=null&&(d=In({inputs:{x:a},backend:n,attrs:{perm:h}}),u=R.getInnerMostAxes(u.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=lP(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,u),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=sd(a.dtype),w=_i(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,c);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var Vj={kernelName:$o,backendName:"webgl",kernelFunc:Bj},M_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=uP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},Uj={kernelName:ku,backendName:"webgl",kernelFunc:M_},jj="return 1.0 / x;",Hj=Xe({opSnippet:jj}),Gj={kernelName:Do,backendName:"webgl",kernelFunc:Hj},qj=gr+`
+`,Pj=en({opSnippet:Oj,packedOpSnippet:zj}),Lj={kernelName:Ds,backendName:"webgl",kernelFunc:Pj};function Wj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=R.getAxesPermutation(c,o),d=a;h!=null&&(d=In({inputs:{x:a},backend:n,attrs:{perm:h}}),c=R.getInnerMostAxes(c.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=oP(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=sd(a.dtype),w=_i(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var Bj={kernelName:$o,backendName:"webgl",kernelFunc:Wj},F_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=lP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},Vj={kernelName:ku,backendName:"webgl",kernelFunc:F_},Uj="return 1.0 / x;",jj=Xe({opSnippet:Uj}),Hj={kernelName:Do,backendName:"webgl",kernelFunc:jj},Gj=gr+`
   return (x < 0.0) ? 0.0 : x;
-`,Xj=`
+`,qj=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3483,9 +3483,9 @@ return a / b;`,cj=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Kj=Xe({opSnippet:qj,packedOpSnippet:Xj}),Zj={kernelName:zs,backendName:"webgl",kernelFunc:Kj},Yj=gr+`
+`,Xj=Xe({opSnippet:Gj,packedOpSnippet:qj}),Kj={kernelName:zs,backendName:"webgl",kernelFunc:Xj},Zj=gr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,Jj=`
+`,Yj=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3495,10 +3495,10 @@ return a / b;`,cj=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Qj=Xe({opSnippet:Yj,packedOpSnippet:Jj}),eH={kernelName:Ls,backendName:"webgl",kernelFunc:Qj},tH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,Jj=Xe({opSnippet:Zj,packedOpSnippet:Yj}),Qj={kernelName:Ls,backendName:"webgl",kernelFunc:Jj},eH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${c[0]/u[0]},
-          ${c[1]/u[1]});
+          ${u[0]/c[0]},
+          ${u[1]/c[1]});
       const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
 
       void main() {
@@ -3528,11 +3528,11 @@ return a / b;`,cj=`
 
         setOutput(newValue);
       }
-    `}},nH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},tH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${c[0]/u[0]},
-          ${c[1]/u[1]},
-          ${c[1]/u[1]});
+          ${u[0]/c[0]},
+          ${u[1]/c[1]},
+          ${u[1]/c[1]});
       const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                      ${o}.0);
 
@@ -3605,7 +3605,7 @@ return a / b;`,cj=`
 
         setOutput(newValue);
       }
-    `}};function rH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new nH(a.shape,l,c,s,i):new tH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var aH={kernelName:Ps,backendName:"webgl",kernelFunc:rH},sH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function nH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new tH(a.shape,l,u,s,i):new eH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var rH={kernelName:Ps,backendName:"webgl",kernelFunc:nH},aH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3615,8 +3615,8 @@ return a / b;`,cj=`
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${c});
-        const float widthScale = float(${u});
+        const float heightScale = float(${u});
+        const float widthScale = float(${c});
 
         const float invHeightScale = float(${h});
         const float invWidthScale = float(${d});
@@ -3686,10 +3686,10 @@ return a / b;`,cj=`
 
         setOutput(accumulator);
       }
-    `}};function iH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new sH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var oH={kernelName:Zh,backendName:"webgl",kernelFunc:iH},lH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function sH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new aH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var iH={kernelName:Zh,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${c[0]/u[0]},
-          ${c[1]/u[1]});
+          ${u[0]/c[0]},
+          ${u[1]/c[1]});
       const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
 
       void main() {
@@ -3708,7 +3708,7 @@ return a / b;`,cj=`
 
         setOutput(newValue);
       }
-    `}};function uH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new lH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var cH={kernelName:Iu,backendName:"webgl",kernelFunc:uH},hH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function lH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new oH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var uH={kernelName:Iu,backendName:"webgl",kernelFunc:lH},cH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3718,8 +3718,8 @@ return a / b;`,cj=`
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${c});
-        const float widthScale = float(${u});
+        const float heightScale = float(${u});
+        const float widthScale = float(${c});
 
         const float invHeightScale = float(${h});
         const float invWidthScale = float(${d});
@@ -3778,7 +3778,7 @@ return a / b;`,cj=`
 
         setOutput(accumulator);
       }
-    `}};function dH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new hH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var pH={kernelName:Kh,backendName:"webgl",kernelFunc:dH},fH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function hH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new cH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var dH={kernelName:Kh,backendName:"webgl",kernelFunc:hH},pH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -3788,7 +3788,7 @@ return a / b;`,cj=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},mH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=dn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
+    `}},fH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=dn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3809,14 +3809,14 @@ return a / b;`,cj=`
             result.g = ${l(r.slice())};
           }
           if(${s}) {
-            result.b = ${c(r.slice())};
+            result.b = ${u(r.slice())};
             if(${a}) {
-              result.a = ${u(r.slice())};
+              result.a = ${c(r.slice())};
             }
           }
           setOutput(result);
         }
-    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function AH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return On({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mH(a.shape,o):new fH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var yH={kernelName:Ws,backendName:"webgl",kernelFunc:AH},gH=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function mH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return On({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fH(a.shape,o):new pH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var AH={kernelName:Ws,backendName:"webgl",kernelFunc:mH},yH=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         uniform vec4 params;
@@ -3836,7 +3836,7 @@ return a / b;`,cj=`
           }
           setOutput(outputValue);
         }
-    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},xH={kernelName:Zo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new gH(r.shape,s),[c,u]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},wH=`
+    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},gH={kernelName:Zo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new yH(r.shape,s),[u,c]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},xH=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3851,7 +3851,7 @@ return a / b;`,cj=`
       return base + 1.0;
     }
   }
-`,bH=Xe({opSnippet:wH}),_H={kernelName:Bs,backendName:"webgl",kernelFunc:bH},vH="return inversesqrt(x);",kH=Xe({opSnippet:vH,cpuKernelImpl:cP}),IH={kernelName:Vs,backendName:"webgl",kernelFunc:kH},F_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
+`,wH=Xe({opSnippet:xH}),bH={kernelName:Bs,backendName:"webgl",kernelFunc:wH},_H="return inversesqrt(x);",vH=Xe({opSnippet:_H,cpuKernelImpl:uP}),kH={kernelName:Vs,backendName:"webgl",kernelFunc:vH},$_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -3861,7 +3861,7 @@ return a / b;`,cj=`
           for (int i = 0; i < ${e}; i++) {
             int flattenedIndex = 0;
             for (int j = 0; j < ${t}; j++) {
-              int index = round(${u});
+              int index = round(${c});
               flattenedIndex += index * ${p};
             }
             if (flattenedIndex == coords[0]) {
@@ -3871,7 +3871,7 @@ return a / b;`,cj=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function NH(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=R.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new F_(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var SH={kernelName:zo,backendName:"webgl",kernelFunc:NH},TH=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ot(n);this.userCode=`
+      `}};function IH(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new $_(l,o,p.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var NH={kernelName:zo,backendName:"webgl",kernelFunc:IH},SH=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);r=o.join(),a=l.join()}let s=ot(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${r});
@@ -3881,21 +3881,21 @@ return a / b;`,cj=`
           setOutput(getB(${a}));
         }
       }
-    `}};function CH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new TH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],tr(a.dtype,s.dtype))}var EH={kernelName:Po,backendName:"webgl",kernelFunc:CH},RH=`
+    `}};function TH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new SH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],tr(a.dtype,s.dtype))}var CH={kernelName:Po,backendName:"webgl",kernelFunc:TH},EH=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${R.SELU_SCALEALPHA};
   float scale = ${R.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,MH=Xe({opSnippet:RH}),FH={kernelName:Lo,backendName:"webgl",kernelFunc:MH},$H="return 1.0 / (1.0 + exp(-1.0 * x));",DH=Xe({opSnippet:$H}),OH={kernelName:js,backendName:"webgl",kernelFunc:DH},zH=`
+`,RH=Xe({opSnippet:EH}),MH={kernelName:Lo,backendName:"webgl",kernelFunc:RH},FH="return 1.0 / (1.0 + exp(-1.0 * x));",$H=Xe({opSnippet:FH}),DH={kernelName:js,backendName:"webgl",kernelFunc:$H},OH=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,PH=Xe({opSnippet:zH}),LH={kernelName:Vo,backendName:"webgl",kernelFunc:PH},WH=Gb+`
+`,zH=Xe({opSnippet:OH}),PH={kernelName:Vo,backendName:"webgl",kernelFunc:zH},LH=qb+`
   return sin(x);
-`,BH=Xe({opSnippet:WH}),VH={kernelName:Us,backendName:"webgl",kernelFunc:BH},UH=`
+`,WH=Xe({opSnippet:LH}),BH={kernelName:Us,backendName:"webgl",kernelFunc:WH},VH=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,jH=Xe({opSnippet:UH}),HH={kernelName:Bo,backendName:"webgl",kernelFunc:jH},GH=`
+`,UH=Xe({opSnippet:VH}),jH={kernelName:Bo,backendName:"webgl",kernelFunc:UH},HH=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3915,9 +3915,9 @@ return a / b;`,cj=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,qH=Xe({opSnippet:GH}),XH={kernelName:Uo,backendName:"webgl",kernelFunc:qH},KH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=R_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(u.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(u.shape,s,o,!1),f=xe({inputs:{x:u},backend:n,attrs:{shape:h}}),m=In({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},ZH={kernelName:Nu,backendName:"webgl",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new F_(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var JH={kernelName:Yh,backendName:"webgl",kernelFunc:YH};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=mc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var eG={kernelName:jo,backendName:"webgl",kernelFunc:QH},tG="return sqrt(x);",nG=Xe({opSnippet:tG}),rG={kernelName:Hs,backendName:"webgl",kernelFunc:nG},aG="return x * x;",sG=Xe({opSnippet:aG}),iG={kernelName:Su,backendName:"webgl",kernelFunc:sG},$_="return (a - b) * (a - b);",oG=en({opSnippet:$_,packedOpSnippet:$_}),lG={kernelName:Xs,backendName:"webgl",kernelFunc:oG};function uG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=gr+`
+`,GH=Xe({opSnippet:HH}),qH={kernelName:Uo,backendName:"webgl",kernelFunc:GH},XH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],c=M_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(c.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(c.shape,s,o,!1),f=xe({inputs:{x:c},backend:n,attrs:{shape:h}}),m=In({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},KH={kernelName:Nu,backendName:"webgl",kernelFunc:XH};function ZH(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new $_(u,l,a.shape.length,s.shape.length,c,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var YH={kernelName:Yh,backendName:"webgl",kernelFunc:ZH};function JH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=mc({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,f})}var QH={kernelName:jo,backendName:"webgl",kernelFunc:JH},eG="return sqrt(x);",tG=Xe({opSnippet:eG}),nG={kernelName:Hs,backendName:"webgl",kernelFunc:tG},rG="return x * x;",aG=Xe({opSnippet:rG}),sG={kernelName:Su,backendName:"webgl",kernelFunc:aG},D_="return (a - b) * (a - b);",iG=en({opSnippet:D_,packedOpSnippet:D_}),oG={kernelName:Xs,backendName:"webgl",kernelFunc:iG};function lG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=gr+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new La(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var cG={kernelName:Na,backendName:"webgl",kernelFunc:uG},hG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
+  `,s=new La(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var uG={kernelName:Na,backendName:"webgl",kernelFunc:lG},cG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -3925,15 +3925,15 @@ return a / b;`,cj=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function dG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=ln.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=mc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=We(w.shape,w.dtype,x),T=dP(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new hG(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var pG={kernelName:Ho,backendName:"webgl",kernelFunc:dG},fG="return tan(x);",mG=Xe({opSnippet:fG}),AG={kernelName:Go,backendName:"webgl",kernelFunc:mG},yG=`
+    `}};function hG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=ln.sliceInfo(a.shape,s,i,o,l,u,c,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=mc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=We(w.shape,w.dtype,x),T=hP(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new cG(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var dG={kernelName:Ho,backendName:"webgl",kernelFunc:hG},pG="return tan(x);",fG=Xe({opSnippet:pG}),mG={kernelName:Go,backendName:"webgl",kernelFunc:fG},AG=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,gG=Xe({opSnippet:yG}),xG={kernelName:Zs,backendName:"webgl",kernelFunc:gG},bG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=wG(e);this.userCode=`
+`,yG=Xe({opSnippet:AG}),gG={kernelName:Zs,backendName:"webgl",kernelFunc:yG},wG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=xG(e);this.userCode=`
       void main() {
         ${r} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function wG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function D_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>v.decodeString(u)),l=We(a.shape,a.dtype,o),c=fP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new bG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var _G={kernelName:Ia,backendName:"webgl",kernelFunc:D_};function vG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=mP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var kG={kernelName:qo,backendName:"webgl",kernelFunc:vG},IG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+    `}};function xG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function O_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(c=>v.decodeString(c)),l=We(a.shape,a.dtype,o),u=pP(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new wG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var bG={kernelName:Ia,backendName:"webgl",kernelFunc:O_};function _G(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=fP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var vG={kernelName:qo,backendName:"webgl",kernelFunc:_G},kG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4045,7 +4045,7 @@ return a / b;`,cj=`
               }
               setOutput(outputValue);
             }
-        `}};function NG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=r,[u,h,d,p]=a.shape,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=new IG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var SG={kernelName:Jh,backendName:"webgl",kernelFunc:NG};function TG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;kl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=AP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var CG={kernelName:Qh,backendName:"webgl",kernelFunc:TG};function EG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=mc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=xe({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var RG={kernelName:Xo,backendName:"webgl",kernelFunc:EG},MG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
+        `}};function IG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=new kG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var NG={kernelName:Jh,backendName:"webgl",kernelFunc:IG};function SG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;kl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=mP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var TG={kernelName:Qh,backendName:"webgl",kernelFunc:SG};function CG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==s&&(u[c++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=mc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=xe({inputs:{x:A},backend:n,attrs:{shape:u}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var EG={kernelName:Xo,backendName:"webgl",kernelFunc:CG},RG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,h=`
         sumValue += dot(values, segFilter);
     `,d="";a%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4078,7 +4078,7 @@ return a / b;`,cj=`
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${c}; i += 4) {
+        for (int i = 0; i < ${u}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -4097,8 +4097,8 @@ return a / b;`,cj=`
           ${h}
         }
 
-        int inIdx = inOffset + ${c};
-        if (${u===1}) {
+        int inIdx = inOffset + ${u};
+        if (${c===1}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             initializationValue,
@@ -4116,7 +4116,7 @@ return a / b;`,cj=`
           );
 
           ${h}
-        } else if (${u===2}) {
+        } else if (${c===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -4132,7 +4132,7 @@ return a / b;`,cj=`
           );
 
           ${h}
-        } else if (${u===3}) {
+        } else if (${c===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -4151,26 +4151,26 @@ return a / b;`,cj=`
         }
         setOutput(${l});
       }
-    `}};function FG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=R.getAxesPermutation([c],o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=R.getInnerMostAxes(1,o)[0]);let d=R.segment_util.computeOutShape(h.shape,c,i),p=v.sizeFromShape([h.shape[c]]),f=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=sd(a.dtype),A=(b,_,x,N,T)=>{let C=b.shape[0],F=b.shape[1],P=R.segment_util.segOpComputeOptimalWindowSize(F,T),W={windowSize:P,inSize:F,batchSize:C,numSegments:T},V=new MG(W,_),U=n.compileAndRun(V,[b,x],N);if(l.push(U),U.shape[1]===T)return U;let H=M_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=D_({inputs:{x:H},backend:n,attrs:{reps:[F/P]}});return l.push(H),l.push(X),A(U,_,X,N,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=xe({inputs:{x:y},backend:n,attrs:{shape:d}}),w=g;if(u!=null){l.push(g);let b=R.getUndoAxesPermutation(u);w=In({inputs:{x:w},backend:n,attrs:{perm:b}})}return 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jX={kernelName:Ro,backendName:"wasm",setupFunc:VX,kernelFunc:UX},HX=!1,GX=pn(To,HX,"bool"),h3;function qX(e){h3=e.wasm.cwrap(Fs,null,["number","number","number","number","number"])}function XX(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(a.dataId).id;return h3(c,s,i,o,u),l}var KX={kernelName:Fs,backendName:"wasm",setupFunc:qX,kernelFunc:XX};function ZX(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var YX={kernelName:Mo,backendName:"wasm",kernelFunc:ZX};function JX(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return pA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let 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cK={kernelName:$o,backendName:"wasm",setupFunc:lK,kernelFunc:uK},hK=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Om(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},dK={kernelName:ku,backendName:"wasm",kernelFunc:hK},pK=!0,fK=pn(ys,pK),mK=Nn(zs),AK=Nn(Ls),m3;function yK(e){m3=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number","number","number","number","number"])}function gK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,[c,h,d,p]=a.shape,f=[c,l,u,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=bp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(v.sizeFromShape(a.shape)===0)return g;let w=t.dataIdMap.get(g.dataId).id;return m3(y,c,h,d,p,l,u,s?1:0,i?1:0,w),A!=null&&t.disposeData(A.dataId),g}var xK={kernelName:Ps,backendName:"wasm",setupFunc:yK,kernelFunc:gK},A3;function 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function R7(e,t){return hte(e,t,"classWeight")}async function M7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());_e(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Ci(c),h=Rc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=dte(f,a[p]));let m=vt(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=vt(m(r[A],h[A]))}Ut(f),s.push(f)}return d=vt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let 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stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ua(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ua(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ua(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)a[s]=ki(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=wn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await wn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Ite,generatedBy:`TensorFlow.js tfjs-layers v${XA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await wn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=wn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;S7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){S7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ca.className="Model";re.registerClass(ca);var W7=class extends ca{};W7.className="Functional";re.registerClass(W7);async function Nte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Ec(n),a=kr(r,t);if(e.weightsManifest!=null){let s=await wn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),_e(s)}return a}async function Tte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=wn.getLoadHandlers(e,t);if(n.length===0)n.push(wn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Ste(e,void 0,t)}async function Ste(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=kr(Ec(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};oy.className="ThresholdedReLU";re.registerClass(oy);var ly=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ey().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ly.className="Softmax";re.registerClass(ly);function Vl(e,t,n){if(typeof e=="number")return vi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Ir(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ua([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function uy(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function nv(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function Ote(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=wr()),St(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=fd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function rv(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=uy(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=za.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function zte(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=nv(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Jf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var cy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",cy.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Vl(t.kernelSize,e,"kernelSize"),this.strides=Vl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Kn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=Ga(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=Vl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!gA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ha(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},$c=class extends cy{constructor(e,t){super(e,t);this.kernel=null,$c.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=V3(this.activation.getClassName());if(a!=null&&this.rank===2)n=rv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Ote(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=rv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=zte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Ir(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Lt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Dc=class extends $c{constructor(e){super(2,e);Dc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!gA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Dc.className="Conv2D";re.registerClass(Dc);var Xp=class extends $c{constructor(e){super(3,e);Xp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xp.className="Conv3D";re.registerClass(Xp);var hy=class extends Dc{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qp(o,h,c,this.padding),f=qp(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=md(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qp(t[r],o,s,this.padding),t[a]=qp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};hy.className="Conv2DTranspose";re.registerClass(hy);var av=class extends $c{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Gt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=Am(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseConstraint),e.pointwiseConstraint=Lt(this.pointwiseConstraint),e}};av.className="SeparableConv";var dy=class extends av{constructor(e){super(2,e)}};dy.className="SeparableConv2D";re.registerClass(dy);var Kp=class extends $c{constructor(e){super(1,e);Kp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!gA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv1D";re.registerClass(Kp);var py=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ip(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};py.className="Cropping2D";re.registerClass(py);var fy=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,aQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};fy.className="UpSampling2D";re.registerClass(fy);function Pte(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=wr()),St(a);let i=uy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=cl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var my=class extends cy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=lt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=Pte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ir(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseRegularizer),e}};my.className="DepthwiseConv2D";re.registerClass(my);function sv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function iv(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(_r(2,l));if(t=nt(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Yt(a,-1)),a=nt(a,c)),r&&(t=$n(t,0),a!=null&&(a=$n(a,0)));let u=[],h,d=n,p=t.shape[0],f=ar(t),m;a!=null&&(m=ar(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=Fn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((C,F)=>w[1][F].mul(_).add(C.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&u.push(h)}let A;return o&&(A=un(u,1)),[h,A,d]})}var jr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Gt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return _r(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){PA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");PA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Gt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Gt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ut(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=sv(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Gt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof vr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=iv((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Ct(e.shape);return t=Ce(t,[1,2]),t=Ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?kA(t,[1,n]):t):this.cell.stateSize>1?[kA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===jr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=kr(r,n);return new e(Object.assign(t,{cell:a}))}};jr.className="RNN";re.registerClass(jr);var Tc=class extends qe{},Yp=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(O(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=O(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";re.registerClass(Yp);var Ay=class extends jr{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Ay.className="SimpleRNN";re.registerClass(Ay);var Jp=class extends Tc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let c=Vr(e,this.kernel.read());this.useBias&&(c=Ur(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=zt(u,[2*this.units,this.units],u.rank-1),p=Vr(r,h),[f,m,A]=zt(c,3,c.rank-1),[y,g]=zt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Vr(O(o,r),d);l=this.activation.apply(se(A,w));let b=se(O(i,r),O(se(1,_t(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Jp.className="GRUCell";re.registerClass(Jp);var yy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};yy.className="GRU";re.registerClass(yy);var Oc=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends or{apply(i,o){let l=a.apply([s]),c=new Sp().apply([s]),u=a.apply([s*2]);return J3(J3(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=zt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(O(l,a),O(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=O(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Oc.className="LSTMCell";re.registerClass(Oc);var gy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Oc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};gy.className="LSTM";re.registerClass(gy);var Zp=class extends Tc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){PA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ni(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(kr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return LA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}WA(t)}};Zp.className="StackedRNNCells";re.registerClass(Zp);function qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>e7(t(),n),i=()=>Sc(s,t,r);return!a||a<=1?Ut(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ut(o.clone()))}var Lte=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},ov=class extends jr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Gt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Ir(l,r[0],a,s[0],i[0]),h=Ir(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};ov.className="ConvRNN2D";var Qp=class extends Oc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Vl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Vl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",Kn(this.padding),this.dataFormat=s||"channelsLast",St(this.dataFormat),this.dilationRate=Vl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=lt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends or{apply(u,h){let d=l.apply([c]),p=Or([c]),f=l.apply([c*2]);return NA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:O(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=zt(this.kernel.read(),i,g),[N,T,C,F]=this.useBias?zt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,N,this.padding),u=this.inputConv(u,b,T,this.padding),h=this.inputConv(h,_,C,this.padding),d=this.inputConv(d,x,F,this.padding);let[P,W,V,U]=zt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,P),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(O(X,s),O(H,this.activation.apply(se(h,A)))),ee=O(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Lte(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=na(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return na(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var xy=class extends ov{constructor(e){let t=new Qp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};xy.className="ConvLSTM2D";re.registerClass(xy);var e0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Sc(()=>e7(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};e0.className="Dropout";re.registerClass(e0);var wy=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};wy.className="SpatialDropout1D";re.registerClass(wy);var by=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=V3(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};by.className="Dense";re.registerClass(by);var _y=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Va(e,1)]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return cQ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};_y.className="Flatten";re.registerClass(_y);var vy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ga(e.activation)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ha(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Activation";re.registerClass(vy);var ky=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return z(()=>(e=Pe(e),lQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};ky.className="RepeatVector";re.registerClass(ky);var Iy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Va(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Reshape";re.registerClass(Iy);var Ny=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=_r(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Gt({ndim:this.dims.length+1})]}computeOutputShape(e){e=lt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Permute";re.registerClass(Ny);var Sy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Wu(di(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Wu(di(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Sy.className="Masking";re.registerClass(Sy);var Ty=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Ht(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ht(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=Pe(e),di(e,je(e))):null)}computeOutputShape(e){if(e=lt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=kc(n,"int32")),Q3(this.embeddings.read(),n.as1D()).reshape(lt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Lt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="Embedding";re.registerClass(Ty);var Ri=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[lt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ba(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ba(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return z(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ua(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Ic(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Va(c.slice(1))));d=nt(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=_r(1,l).concat([0]);n.push(nt(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=nt(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(_r(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ba(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return z(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Yt(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=rr(n,t[r]);return n})}},Cy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};Cy.className="Add";re.registerClass(Cy);var Ey=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=O(t,e[n]);return t})}};Ey.className="Multiply";re.registerClass(Ey);var Ry=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return O(1/e.length,t)})}};Ry.className="Average";re.registerClass(Ry);var My=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Dr(t,e[n]);return t})}};My.className="Maximum";re.registerClass(My);var Fy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ml(t,e[n]);return t})}};Fy.className="Minimum";re.registerClass(Fy);var $y=class extends Ri{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(v.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return z(()=>NA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return z(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(Fn(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Yt(t[s],-1)):r.push(t[s]);let a=rt(r,this.axis);return dd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Concatenate";re.registerClass($y);function zc(e,t){for(;e<0;)e+=t;return e}function Wte(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Dy=class extends Ri{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>zc(a,e[s].shape.length)):r=[zc(this.axes,t.shape.length),zc(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,r[0]),n=Lp(n,r[1])),Wte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[zc(this.axes,e.length),zc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Dy.className="Dot";re.registerClass(Dy);var Oy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Sc(()=>Np(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Oy.className="GaussianNoise";re.registerClass(Oy);var zy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Sc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Np(n.shape,1,r))},()=>n,t.training||!1):n})}};zy.className="GaussianDropout";re.registerClass(zy);var Py=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Sc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Da(Al(n),this.rate);o=kc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Pe(e),t.training||!1)}return e})}};Py.className="AlphaDropout";re.registerClass(Py);function Pc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=J5(e,t,n,r,a,s);else if(e.rank===3)i=Q5(e,t,n,r,a,s);else if(e.rank===4)i=ex(e,t,n,r,a,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Bte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance;return[Pc(e,i,o,n,t,a),i,o]})}function Vte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance,l=[];for(let p of _r(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Pc(e,c,u,d,h,a),i,o]})}function Ute(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),_r(0,e.rank-1))?Bte(e,t,n,r,a):Vte(e,t,n,r,a)}var Ly=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=lt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Gt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=_r(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=vi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!v.arraysEqual(c,_r(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Pc(r,A,y,g,w,this.epsilon)}else return Pc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=Ute(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{z(()=>{let w=1-g,b=A.read(),_=b.sub(y).mul(w);A.write(b.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Lt(this.betaConstraint),gammaConstraint:Lt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="BatchNormalization";re.registerClass(Ly);var Wy=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=lt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ba(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=kd(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Pc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="LayerNormalization";re.registerClass(Wy);function jte(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=wr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=Ku(e,t,n,o):i=Vu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function lv(e,t,n,r,a,s){return z(()=>{St(a),G3(s),Kn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=wr()),s==null&&(s="max"),e=nv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=um(e,t,n,o):i=Kf(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var uv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Kn(this.padding),this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){e=lt(e);let t=Ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Ic(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Oa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends uv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};Vy.className="MaxPooling1D";re.registerClass(Vy);var Uy=class extends uv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"avg")}};Uy.className="AveragePooling1D";re.registerClass(Uy);var cv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};jy.className="MaxPooling2D";re.registerClass(jy);var Hy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"avg")}};Hy.className="AveragePooling2D";re.registerClass(Hy);var hv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:5})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),r=Ir(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),lv(e,t,n,r,a,"max")}};Gy.className="MaxPooling3D";re.registerClass(Gy);var qy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),lv(e,t,n,r,a,"avg")}};qy.className="AveragePooling3D";re.registerClass(qy);var dv=class extends qe{constructor(e){super(e);this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Xy=class extends dv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vt(n,1)})}};Xy.className="GlobalAveragePooling1D";re.registerClass(Xy);var Ky=class extends dv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vn(n,1)})}};Ky.className="GlobalMaxPooling1D";re.registerClass(Ky);var pv=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Zy=class extends pv{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?vt(n,[1,2]):vt(n,[2,3])})}};Zy.className="GlobalAveragePooling2D";re.registerClass(Zy);var Yy=class extends pv{call(e,t){return z(()=>{let n=Pe(e);return 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e(s)}},Jy=class extends fv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=lt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=lt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=Pe(e),iv((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Jy.className="TimeDistributed";re.registerClass(Jy);function Hte(e){Ii(rQ,"BidirectionalMergeMode",e)}var Gte="concat",Qy=class extends fv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=kr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=kr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Gte:e.mergeMode,Hte(this.mergeMode),e.weights)throw new $e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Sn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=sv(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new 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i;return this.mergeMode==="concat"?i=NA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=O(.5,se(r,a)):this.mergeMode==="mul"?i=O(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ni(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ni(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function M7(e,t){return cte(e,t,"classWeight")}async function F7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());_e(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),sn(i,"float32")}else return null}function hte(e,t){return O(e,t)}var dte=32;function D7(e,t){let n,r,a=t;n=a.xs,r=a.ys,v.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=$7("input",e.inputNames,n),i=$7("output",e.outputNames,r),o=s[0].shape[0];v.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  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Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let p=0;p<this.inputs.length;++p)u.push({key:this.inputs[p],value:n[p]});let c=new Ci(u),h=Rc(this.outputs,c,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=hte(f,a[p]));let m=vt(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=vt(m(r[A],h[A]))}Ut(f),s.push(f)}return d=vt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Ci(s),o=Rc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=vt(u(a[l],o[l]));l===0?n=c:n=se(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],h=vt(u(a[c],o[c]));t.push(h)}return t})}async fit(e,t,n={}){return xte(this,e,t,n)}async fitDataset(e,t){return mte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return _e(s),Sn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ua(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ua(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ua(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)a[s]=ki(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=wn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await wn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:kte,generatedBy:`TensorFlow.js tfjs-layers v${ZA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await wn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=wn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;T7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){T7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ca.className="Model";re.registerClass(ca);var B7=class extends ca{};B7.className="Functional";re.registerClass(B7);async function Ite(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Ec(n),a=kr(r,t);if(e.weightsManifest!=null){let s=await wn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),_e(s)}return a}async function Ste(e,t){if(t==null&&(t={}),typeof e=="string"){let n=wn.getLoadHandlers(e,t);if(n.length===0)n.push(wn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Nte(e,void 0,t)}async function Nte(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=kr(Ec(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};uy.className="ThresholdedReLU";re.registerClass(uy);var cy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ny().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Softmax";re.registerClass(cy);function Vl(e,t,n){if(typeof e=="number")return vi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Ir(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ua([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function hy(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function rv(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function Dte(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=wr()),St(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=fd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function av(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=hy(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=za.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function Ote(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=rv(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=em(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var dy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",dy.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Vl(t.kernelSize,e,"kernelSize"),this.strides=Vl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Kn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=Ga(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=Vl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!wA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ha(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},$c=class extends dy{constructor(e,t){super(e,t);this.kernel=null,$c.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=U3(this.activation.getClassName());if(a!=null&&this.rank===2)n=av(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Dte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=av(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Ote(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Ir(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Lt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Dc=class extends $c{constructor(e){super(2,e);Dc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!wA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Dc.className="Conv2D";re.registerClass(Dc);var Xp=class extends $c{constructor(e){super(3,e);Xp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xp.className="Conv3D";re.registerClass(Xp);var py=class extends Dc{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qp(o,h,u,this.padding),f=qp(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=md(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qp(t[r],o,s,this.padding),t[a]=qp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};py.className="Conv2DTranspose";re.registerClass(py);var sv=class extends $c{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Gt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=gm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseConstraint),e.pointwiseConstraint=Lt(this.pointwiseConstraint),e}};sv.className="SeparableConv";var fy=class extends sv{constructor(e){super(2,e)}};fy.className="SeparableConv2D";re.registerClass(fy);var Kp=class extends $c{constructor(e){super(1,e);Kp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!wA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv1D";re.registerClass(Kp);var my=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ip(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};my.className="Cropping2D";re.registerClass(my);var Ay=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,rQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="UpSampling2D";re.registerClass(Ay);function zte(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=wr()),St(a);let i=hy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=cl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var yy=class extends dy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=lt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=zte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ir(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseRegularizer),e}};yy.className="DepthwiseConv2D";re.registerClass(yy);function iv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function ov(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(_r(2,l));if(t=nt(t,u),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Yt(a,-1)),a=nt(a,u)),r&&(t=$n(t,0),a!=null&&(a=$n(a,0)));let c=[],h,d=n,p=t.shape[0],f=ar(t),m;a!=null&&(m=ar(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=Fn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((C,F)=>w[1][F].mul(_).add(C.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=un(c,1)),[h,A,d]})}var jr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Gt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return _r(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){WA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");WA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Gt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Gt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ut(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=iv(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Gt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof vr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=ov((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return z(()=>{let t=Ct(e.shape);return t=Ce(t,[1,2]),t=Ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?NA(t,[1,n]):t):this.cell.stateSize>1?[NA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===jr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=kr(r,n);return new e(Object.assign(t,{cell:a}))}};jr.className="RNN";re.registerClass(jr);var Tc=class extends qe{},Yp=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(O(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=O(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";re.registerClass(Yp);var gy=class extends jr{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};gy.className="SimpleRNN";re.registerClass(gy);var Jp=class extends Tc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let u=Vr(e,this.kernel.read());this.useBias&&(u=Ur(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let c=this.recurrentKernel.read(),[h,d]=zt(c,[2*this.units,this.units],c.rank-1),p=Vr(r,h),[f,m,A]=zt(u,3,u.rank-1),[y,g]=zt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Vr(O(o,r),d);l=this.activation.apply(se(A,w));let b=se(O(i,r),O(se(1,_t(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Jp.className="GRUCell";re.registerClass(Jp);var xy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};xy.className="GRU";re.registerClass(xy);var Oc=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends or{apply(i,o){let l=a.apply([s]),u=new Sp().apply([s]),c=a.apply([s*2]);return Q3(Q3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=zt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),u=se(O(l,a),O(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let A=O(c,this.activation.apply(u));return[A,A,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Oc.className="LSTMCell";re.registerClass(Oc);var wy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Oc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};wy.className="LSTM";re.registerClass(wy);var Zp=class extends Tc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){WA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ni(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(kr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return BA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}VA(t)}};Zp.className="StackedRNNCells";re.registerClass(Zp);function qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>t7(t(),n),i=()=>Sc(s,t,r);return!a||a<=1?Ut(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ut(o.clone()))}var Pte=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},lv=class extends jr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Gt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=Ir(l,r[0],a,s[0],i[0]),h=Ir(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};lv.className="ConvRNN2D";var Qp=class extends Oc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Vl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Vl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",Kn(this.padding),this.dataFormat=s||"channelsLast",St(this.dataFormat),this.dilationRate=Vl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=lt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends or{apply(c,h){let d=l.apply([u]),p=Or([u]),f=l.apply([u*2]);return TA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:O(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=zt(this.kernel.read(),i,g),[N,T,C,F]=this.useBias?zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,w,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,_,C,this.padding),d=this.inputConv(d,x,F,this.padding);let[P,W,V,U]=zt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,P),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(se(u,f)),X=this.recurrentActivation.apply(se(c,m)),G=se(O(X,s),O(j,this.activation.apply(se(h,A)))),ee=O(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Pte(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=na(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return na(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var by=class extends lv{constructor(e){let t=new Qp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};by.className="ConvLSTM2D";re.registerClass(by);var e0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Sc(()=>t7(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};e0.className="Dropout";re.registerClass(e0);var _y=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};_y.className="SpatialDropout1D";re.registerClass(_y);var vy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=U3(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Dense";re.registerClass(vy);var ky=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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z(()=>(e=Pe(e),oQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="RepeatVector";re.registerClass(Ny);var Sy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Va(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Reshape";re.registerClass(Sy);var Ty=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=_r(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Gt({ndim:this.dims.length+1})]}computeOutputShape(e){e=lt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="Permute";re.registerClass(Ty);var Cy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Wu(di(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Wu(di(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Cy.className="Masking";re.registerClass(Cy);var Ey=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Ht(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ht(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=Pe(e),di(e,je(e))):null)}computeOutputShape(e){if(e=lt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=kc(n,"int32")),e7(this.embeddings.read(),n.as1D()).reshape(lt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Lt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Embedding";re.registerClass(Ey);var Ri=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[lt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ba(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Concatenate";re.registerClass(Oy);function zc(e,t){for(;e<0;)e+=t;return e}function Lte(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c<l+i;++c)u.push(c);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var zy=class extends Ri{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Py=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Sc(()=>Np(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Py.className="GaussianNoise";re.registerClass(Py);var Ly=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Sc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Np(n.shape,1,r))},()=>n,t.training||!1):n})}};Ly.className="GaussianDropout";re.registerClass(Ly);var Wy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Sc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Da(Al(n),this.rate);o=kc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Pe(e),t.training||!1)}return e})}};Wy.className="AlphaDropout";re.registerClass(Wy);function Pc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=Q5(e,t,n,r,a,s);else if(e.rank===3)i=ex(e,t,n,r,a,s);else if(e.rank===4)i=tx(e,t,n,r,a,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Wte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance;return[Pc(e,i,o,n,t,a),i,o]})}function Bte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance,l=[];for(let p of _r(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Pc(e,u,c,d,h,a),i,o]})}function Vte(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),_r(0,e.rank-1))?Wte(e,t,n,r,a):Bte(e,t,n,r,a)}var By=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=lt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Gt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=_r(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=vi(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,_r(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Pc(r,A,y,g,w,this.epsilon)}else return 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extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=lt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ba(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=kd(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),c=c.tile(p),h=h.tile(p),Pc(n,i,o,h,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="LayerNormalization";re.registerClass(Vy);function Ute(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=wr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=Ku(e,t,n,o):i=Vu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function uv(e,t,n,r,a,s){return z(()=>{St(a),q3(s),Kn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=wr()),s==null&&(s="max"),e=rv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=hm(e,t,n,o):i=Yf(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var cv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Kn(this.padding),this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){e=lt(e);let t=Ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Ic(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Oa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};jy.className="MaxPooling1D";re.registerClass(jy);var Hy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};Gy.className="MaxPooling2D";re.registerClass(Gy);var qy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"avg")}};qy.className="AveragePooling2D";re.registerClass(qy);var dv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:5})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),r=Ir(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Xy=class extends dv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),uv(e,t,n,r,a,"max")}};Xy.className="MaxPooling3D";re.registerClass(Xy);var Ky=class extends dv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),uv(e,t,n,r,a,"avg")}};Ky.className="AveragePooling3D";re.registerClass(Ky);var pv=class extends qe{constructor(e){super(e);this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Zy=class extends pv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vt(n,1)})}};Zy.className="GlobalAveragePooling1D";re.registerClass(Zy);var Yy=class extends pv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vn(n,1)})}};Yy.className="GlobalMaxPooling1D";re.registerClass(Yy);var fv=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jy=class extends fv{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?vt(n,[1,2]):vt(n,[2,3])})}};Jy.className="GlobalAveragePooling2D";re.registerClass(Jy);var Qy=class extends fv{call(e,t){return z(()=>{let n=Pe(e);return 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e(s)}},e2=class extends mv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=lt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=lt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=Pe(e),ov((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};e2.className="TimeDistributed";re.registerClass(e2);function jte(e){Ii(nQ,"BidirectionalMergeMode",e)}var Hte="concat",t2=class extends mv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=kr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=kr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Hte:e.mergeMode,jte(this.mergeMode),e.weights)throw new $e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Sn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=iv(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new 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i;return this.mergeMode==="concat"?i=TA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=O(.5,se(r,a)):this.mergeMode==="mul"?i=O(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ni(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ni(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Ene=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],Wv={};Oe(Wv,{json:()=>Rne});var Rne=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],Vv=class{static 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d={};e.library!=null&&e.library.function!=null&&(d=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let p={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:d};return s.length>0&&(p.initNodes=s),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=vv(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(r=>r.startsWith("^")?r.substr(1):r),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((r,a)=>(r[a.name]={type:a.type,inputIndexStart:a.start,inputIndexEnd:a.end},r),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((r,a)=>{let s=a.type,i;switch(a.type){case"string":i=r2(e.attr,a.tfName,a.defaultValue),i===void 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s=[],i=[];e.signature.inputArg.forEach(u=>{let[c]=ha(u.name),h={name:c,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:i2(u.type),type:"dtype"}},children:[]};h.signatureKey=u.name,s.push(h),a[c]=h}),Object.keys(a).forEach(u=>{let c=a[u];c.inputNames.forEach(h=>{let[d]=ha(h);c.inputs.push(a[d]),a[d].children.push(c)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[c,h]=ha(o[u.name]),d=a[c];d!=null&&(d.defaultOutput=h,i.push(d))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:r,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function Mne(e){let t=J().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function Uv(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Mne(e);return t?n:n.toLowerCase()}function r2(e,t,n,r=!1){let a=e[t];return a!=null?Uv(a.s,r):n}function a2(e,t,n){let r=e[t];return r?r.b:n}function s2(e,t,n){let r=e[t]||{},a=r.i!=null?r.i:r.f!=null?r.f:n;return typeof a=="number"?a:parseInt(a,10)}function i2(e){switch(typeof e=="string"&&(e=Nr[e]),e){case Nr.DT_FLOAT:return"float32";case Nr.DT_INT32:case Nr.DT_INT64:case Nr.DT_INT8:case Nr.DT_UINT8:return"int32";case Nr.DT_BOOL:return"bool";case Nr.DT_DOUBLE:return"float32";case Nr.DT_STRING:return"string";default:return null}}function Bv(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function o2(e,t,n){let r=e[t];return r&&r.type?i2(r.type):n}function l2(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>i2(a)):n}function jv(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function u2(e,t,n){let r=e[t];return r&&r.shape?jv(r.shape):n}function c2(e,t,n){let r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(a=>typeof a=="number"?a:parseInt(a,10)):n}function h2(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>Uv(s,r)):n}function d2(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>jv(a)):n}function p2(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var Fne=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(r=>this.getInput(r)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((r,a)=>(r[a]=this.getAttr(a),r),{}))}getInput(e){return Cn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Cn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return s2(this.node.rawAttrs,e,t);if(n.s!=null)return r2(this.node.rawAttrs,e,t);if(n.b!=null)return a2(this.node.rawAttrs,e,t);if(n.shape!=null)return u2(this.node.rawAttrs,e,t);if(n.type!=null)return o2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return c2(this.node.rawAttrs,e,t);if(n.list.s!=null)return h2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return d2(this.node.rawAttrs,e,t);if(n.list.b!=null)return p2(this.node.rawAttrs,e,t);if(n.list.type!=null)return l2(this.node.rawAttrs,e,t)}return t}},$ne=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(k("a",e,t,n),k("b",e,t,n))];case"AddN":return[Ra(k("tensors",e,t,n))];case"FloorMod":case"Mod":return[pm(k("a",e,t,n),k("b",e,t,n))];case"Mul":return[O(k("a",e,t,n),k("b",e,t,n))];case"RealDiv":case"Div":return[me(k("a",e,t,n),k("b",e,t,n))];case"DivNoNan":return[rm(k("a",e,t,n),k("b",e,t,n))];case"FloorDiv":return[hd(k("a",e,t,n),k("b",e,t,n))];case"Sub":return[Ae(k("a",e,t,n),k("b",e,t,n))];case"Minimum":return[ml(k("a",e,t,n),k("b",e,t,n))];case"Maximum":return[Dr(k("a",e,t,n),k("b",e,t,n))];case"Pow":return[aa(k("a",e,t,n),k("b",e,t,n))];case"SquaredDifference":return[Dd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Dne=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Dt(k("x",e,t,n))];case"Acos":return[Bf(k("x",e,t,n))];case"Acosh":return[Vf(k("x",e,t,n))];case"Asin":return[jf(k("x",e,t,n))];case"Asinh":return[Hf(k("x",e,t,n))];case"Atan":return[Gf(k("x",e,t,n))];case"Atan2":return[qf(k("x",e,t,n),k("y",e,t,n))];case"Atanh":return[Xf(k("x",e,t,n))];case"Ceil":return[Jf(k("x",e,t,n))];case"Complex":return[Sa(k("real",e,t,n),k("imag",e,t,n))];case"Cos":return[Hu(k("x",e,t,n))];case"Cosh":return[Ad(k("x",e,t,n))];case"Elu":return[hl(k("x",e,t,n))];case"Erf":return[am(k("x",e,t,n))];case"Exp":return[Gn(k("x",e,t,n))];case"Expm1":return[sm(k("x",e,t,n))];case"Floor":return[dl(k("x",e,t,n))];case"Log":return[Mn(k("x",e,t,n))];case"Log1p":return[wd(k("x",e,t,n))];case"Imag":return[gd(k("x",e,t,n))];case"Neg":return[_t(k("x",e,t,n))];case"Reciprocal":return[Am(k("x",e,t,n))];case"Real":return[Ju(k("x",e,t,n))];case"Relu":return[zr(k("x",e,t,n))];case"Round":return[ym(k("x",e,t,n))];case"Selu":return[Cd(k("x",e,t,n))];case"Sigmoid":return[Rn(k("x",e,t,n))];case"Sin":return[Ed(k("x",e,t,n))];case"Sign":return[xm(k("x",e,t,n))];case"Sinh":return[Rd(k("x",e,t,n))];case"Softplus":return[pl(k("x",e,t,n))];case"Sqrt":return[Jt(k("x",e,t,n))];case"Square":return[it(k("x",e,t,n))];case"Tanh":return[ll(k("x",e,t,n))];case"Tan":return[_m(k("x",e,t,n))];case"ClipByValue":return[bn(k("x",e,t,n),k("clipValueMin",e,t,n),k("clipValueMax",e,t,n))];case"Relu6":return[Sd(k("x",e,t,n))];case"Rsqrt":return[Td(Cn(e.inputNames[0],t,n))];case"Prod":return[Id(k("x",e,t,n),k("axes",e,t,n))];case"LeakyRelu":return[qu(k("x",e,t,n),k("alpha",e,t,n))];case"Prelu":return[Yu(k("x",e,t,n),k("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ur(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let r=0;r<e.length;r++){let a=e[r],s=t[r];v.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Hv(e){return!(typeof e=="number"||e.some(t=>t<0))}function Lc(e,t,n){let r=f2(e,n),a=!Hv(r);if(a&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(a&&t.forEach(s=>{r=f2(s.shape,r)}),!Hv(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function f2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let r=0;r<e.length;++r){let a=e[r],s=t[r];if(a>=0&&s>=0&&a!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=a>=0?a:s}return n}var 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r=k("size",e,t,n),a=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),c=k("name",e,t,n),u=new zne(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ge(1)]}case"TensorArrayWriteV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let 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r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=Lne(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=k("tensorListId",e,t,n),a=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=Pne(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=k("tensorListId",e,t,n),a=n.getTensorList(r.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=k("tensorListId",e,t,n),a=k("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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ire(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Ln(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var ore=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],lre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],ure=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Xv(e){return ore.indexOf(e.op)>=0}function are(e){return lre.indexOf(e.op)>=0}function sre(e){return ure.indexOf(e.op)>=0}var f2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ha(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Ln(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Ln(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Ln(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},cre=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},hre="?tfjs-format=file",dre="model.json",Zv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new cre}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=wn.browserHTTPRequest(e,this.loadOptions);else{let t=wn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(wn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=wn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new f2(Bv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Bv.Instance.transformGraph(e.modelInitializer);this.initializer=new f2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=wn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ve)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=al.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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m6{constructor(e,t){super();this.upstream=e,this.impl=new qre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qre=class extends y2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Kre=class extends qt{decodeUTF8(){return new Xre(this)}},Xre=class extends m6{constructor(e){super();this.upstream=e,this.impl=new Zre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zre=class extends y2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=D8();this.decoder=new 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lae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],uae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],cae=[33,133,362,263,1,78,308],the=lae.map(e=>W2[e]),nhe=uae.map(e=>W2[e]),rhe=cae.map(e=>W2[e]);var B2=Xr.leftEyeLower0,V2=Xr.rightEyeLower0,ql={leftBounds:[B2[0],B2[B2.length-1]],rightBounds:[V2[0],V2[V2.length-1]]},_0={count:468,mouth:13,symmetryLine:[13,Xr.midwayBetweenEyes[0]]},C6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Xl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function v0(e,t,n,r){for(let a=0;a<L2.length;a++){let{key:s,indices:i}=L2[a],o=Xr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var U2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Vc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?b0(r,[0,0]):w0,l=r!==0?i.map(h=>[...T6(h,o),h[2]]):i,c=r!==0?S6(a):w0,u=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(u,c[0]),h[1]+Ya(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ql.leftBounds[0]][2],r=t[ql.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=x0(g0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Vc(i),l=ze.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&fr.flags.IS_BROWSER&&(l=ze.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Xl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Xl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Xr[`${r}EyeUpper0`][Xl.upperCenter]][2],s=t[Xr[`${r}EyeLower0`][Xl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=k6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=g0(o),c=x0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=_0.count?_0.symmetryLine:C6.symmetryLine;u=P2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,u,0,T);h=b0(-u,N),n.face.mesh.enabled?c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=w0;let _=t.clone();n.face.mesh.enabled?c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=j(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,c,ql.leftBounds[0],ql.leftBounds[1],!0),{box:T,boxSize:C,crop:F}=this.getEyeBox(A,c,ql.rightBounds[0],ql.rightBounds[1]),W=this.irisModel.predict(rt([N,F])).dataSync(),V=W.slice(0,Xl.numCoordinates*3),{rawCoords:U,iris:H}=this.getEyeCoords(V,_,x,!0),X=W.slice(Xl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,C),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(v0(A,U,"left",null),v0(A,G,"right",null)):Y<1?v0(A,U,"left",["EyeUpper0","EyeLower0"]):v0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,H,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=g0(this.calculateLandmarksBoundingBox(y),1.5);let g=kn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=_0.count?_0.symmetryLine:C6.symmetryLine;u=P2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,u,0,T);h=b0(-u,N),c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},b=x0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var H2=class{constructor(t,n,r,a){this.facePipeline=new U2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Xr))l[h]=Xr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Fi=[null,null,null];async function G2(e){Fi=await Promise.all([!Fi[0]&&e.face.enabled?v6(e):null,!Fi[1]&&e.face.mesh.enabled?ct(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Fi[2]&&e.face.iris.enabled?ct(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new H2(Fi[0],Fi[1],Fi[2],e);return e.face.mesh.enabled&&e.debug&&Ne(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ne(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var hae=Mi;var ng={};pr(ng,{PoseNet:()=>rg,load:()=>ag});function dae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var q2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=dae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function X2(e){return Math.floor(e/2)}var K2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(X2(t),t);)this.exchange(t,X2(t)),t=X2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function pae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function E6(e,t,n){let[r,a,s]=n.shape,i=new K2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||pae(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var Kl=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Zl=Kl.length,Uc=Kl.reduce((e,t,n)=>(e[t]=n,e),{}),fae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],whe=fae.map(([e,t])=>[Uc[e],Uc[t]]),R6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Z2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function k0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=Z2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function Y2(e,t,n){return e<t?t:e>n?n:e}function M6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function J2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function F6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function mae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function Aae(e,t){let n=[];for(let r=0;r<Zl;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=mae(a,s,r,t);n.push(o),n.push(i)}return kn(n,[Zl,2])}function $6(e,t,n){return z(()=>e.toTensor().mul(ge(t,"int32")).toFloat().add(Aae(e,n)))}function yae(e,t){return z(()=>{let n=e.div(ge(t,"int32"));return e.sub(n.mul(ge(t,"int32")))})}function D6(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ge(n,"int32")).expandDims(1),o=yae(s,n).expandDims(1);return rt([i,o],1)})}var O6=R6.map(([e,t])=>[Uc[e],Uc[t]]),Q2=O6.map(([,e])=>e),z6=O6.map(([e])=>e),gae=16;function xae(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function eg(e,t,n,r){return{y:Y2(Math.round(e.y/t),0,n-1),x:Y2(Math.round(e.x/t),0,r-1)}}function P6(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=eg(t.position,s,l,c),h=xae(e,u,i),p=J2(t.position,h);for(let A=0;A<o;A++){let y=eg(p,s,l,c),g=Z2(y.y,y.x,n,a);p=J2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=eg(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:Kl[n],score:m}}function L6(e,t,n,r,a,s){let i=t.shape[2],o=Q2.length,l=new Array(i),{part:c,score:u}=e,h=k0(c,r,n);l[c.id]={score:u,part:Kl[c.id],position:h};for(let d=o-1;d>=0;--d){let p=Q2[d],f=z6[d];l[p]&&!l[f]&&(l[f]=P6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=z6[d],f=Q2[d];l[p]&&!l[f]&&(l[f]=P6(d,l[p],f,t,n,r,a))}return l}async function W6(e,t,n){let r=0,a=D6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=$6(l,gae,o),u=await c.buffer(),d=Array.from(F6(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:Kl[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var wae=1,B6=16;function V6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return M6(r,n,i.y,i.x)<=t})}function bae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(V6(e,t,s,o)||(a+=i),a),0)/n.length}function U6(e,t,n,r,a,s,i){let o=[],l=E6(i,wae,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=k0(u.part,B6,t);if(V6(o,c,h,u.part.id))continue;let d=L6(u,e,t,B6,n,r),p=bae(o,c,d);p>i&&o.push({keypoints:d,score:p})}return o}async function j6(e){return Promise.all(e.map(t=>t.buffer()))}function 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r=H6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await kae(t,a,n,this.inputSize):await vae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function ag(e){let t=await ct(e.body.modelPath),n=new q2(t);return e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new rg(n)}var ug={};pr(ug,{HandPose:()=>hg,load:()=>dg});function I0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function jc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function G6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return ze.cropAndResize(t,s,[0],n)}function q6(e,t){let 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g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=j(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let N={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=N0(S0(o),J6),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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t4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],n4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var cr;async function fg(e){return cr||(cr=await ct(e.body.modelPath),cr.width=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[2].size),cr.height=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),cr}async function mg(e,t){if(!cr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=ze.resizeBilinear(e,[cr.width,cr.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let c=await an(()=>cr.predict(a));s=c.result.find(u=>u.size===195||u.size===155).dataSync(),c.result.forEach(u=>u.dispose()),fn("blazepose",c)}else{let c=await cr.predict(a);s=c.find(u=>u.size===195||u.size===155).dataSync(),c.forEach(u=>u.dispose())}a.dispose();let i=[],o=s.length===195?t4:n4,l=5;for(let 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u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ha(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Ln(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},p6=class extends qt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=al.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Yt(ye(e,"float32"),0),n;n=ze.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},m6=class{},A6=class extends qt{split(e){return new Hre(this,e)}},Hre=class extends A6{constructor(e,t){super();this.upstream=e,this.impl=new Gre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Gre=class extends x2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Xre=class extends qt{decodeUTF8(){return new qre(this)}},qre=class extends A6{constructor(e){super();this.upstream=e,this.impl=new Kre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Kre=class extends x2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$8();this.decoder=new 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oae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],lae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],uae=[33,133,362,263,1,78,308],ehe=oae.map(e=>B2[e]),the=lae.map(e=>B2[e]),nhe=uae.map(e=>B2[e]);var V2=Xr.leftEyeLower0,U2=Xr.rightEyeLower0,ql={leftBounds:[V2[0],V2[V2.length-1]],rightBounds:[U2[0],U2[U2.length-1]]},v0={count:468,mouth:13,symmetryLine:[13,Xr.midwayBetweenEyes[0]]},E6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Xl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function k0(e,t,n,r){for(let a=0;a<W2.length;a++){let{key:s,indices:i}=W2[a],o=Xr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var j2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Vc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?_0(r,[0,0]):b0,l=r!==0?i.map(h=>[...C6(h,o),h[2]]):i,u=r!==0?T6(a):b0,c=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(c,u[0]),h[1]+Ya(c,u[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ql.leftBounds[0]][2],r=t[ql.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=w0(x0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Vc(i),l=ze.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&fr.flags.IS_BROWSER&&(l=ze.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Xl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(Xl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Xr[`${r}EyeUpper0`][Xl.upperCenter]][2],s=t[Xr[`${r}EyeLower0`][Xl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=a:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=I6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=x0(o),u=w0(l),c=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:h,landmarks:c}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,u,c=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=v0.count?v0.symmetryLine:E6.symmetryLine;c=L2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,c,0,T);h=_0(-c,N),n.face.mesh.enabled?u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=b0;let _=t.clone();n.face.mesh.enabled?u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:u};let[,d,p]=this.meshDetector.predict(u),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,u,ql.leftBounds[0],ql.leftBounds[1],!0),{box:T,boxSize:C,crop:F}=this.getEyeBox(A,u,ql.rightBounds[0],ql.rightBounds[1]),W=this.irisModel.predict(rt([N,F])).dataSync(),V=W.slice(0,Xl.numCoordinates*3),{rawCoords:U,iris:j}=this.getEyeCoords(V,_,x,!0),X=W.slice(Xl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,C),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(k0(A,U,"left",null),k0(A,G,"right",null)):Y<1?k0(A,U,"left",["EyeUpper0","EyeLower0"]):k0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,j,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,c,h);i=x0(this.calculateLandmarksBoundingBox(y),1.5);let g=kn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=v0.count?v0.symmetryLine:E6.symmetryLine;c=L2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,c,0,T);h=_0(-c,N),u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:u,rawCoords:A},b=w0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var G2=class{constructor(t,n,r,a){this.facePipeline=new j2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Xr))l[h]=Xr[h].map(d=>i[d]);let u=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,c=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:u,boxRaw:c,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Fi=[null,null,null];async function q2(e){Fi=await Promise.all([!Fi[0]&&e.face.enabled?k6(e):null,!Fi[1]&&e.face.mesh.enabled?ct(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Fi[2]&&e.face.iris.enabled?ct(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new G2(Fi[0],Fi[1],Fi[2],e);return e.face.mesh.enabled&&e.debug&&Ne(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ne(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var cae=Mi;var rg={};pr(rg,{PoseNet:()=>ag,load:()=>sg});function hae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var X2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=hae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function K2(e){return Math.floor(e/2)}var Z2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(K2(t),t);)this.exchange(t,K2(t)),t=K2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function dae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,u=Math.max(n-a,0),c=Math.min(n+a+1,i);for(let h=u;h<c;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function R6(e,t,n){let[r,a,s]=n.shape,i=new Z2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let u=0;u<s;++u){let c=n.get(o,l,u);c<e||dae(u,c,o,l,t,n)&&i.enqueue({score:c,part:{heatmapY:o,heatmapX:l,id:u}})}return i}var Kl=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Zl=Kl.length,Uc=Kl.reduce((e,t,n)=>(e[t]=n,e),{}),pae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],xhe=pae.map(([e,t])=>[Uc[e],Uc[t]]),M6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Y2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function I0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=Y2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function J2(e,t,n){return e<t?t:e>n?n:e}function F6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function Q2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function $6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function fae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function mae(e,t){let n=[];for(let r=0;r<Zl;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=fae(a,s,r,t);n.push(o),n.push(i)}return kn(n,[Zl,2])}function D6(e,t,n){return z(()=>e.toTensor().mul(ge(t,"int32")).toFloat().add(mae(e,n)))}function Aae(e,t){return z(()=>{let n=e.div(ge(t,"int32"));return e.sub(n.mul(ge(t,"int32")))})}function O6(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ge(n,"int32")).expandDims(1),o=Aae(s,n).expandDims(1);return rt([i,o],1)})}var z6=M6.map(([e,t])=>[Uc[e],Uc[t]]),eg=z6.map(([,e])=>e),P6=z6.map(([e])=>e),yae=16;function gae(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function tg(e,t,n,r){return{y:J2(Math.round(e.y/t),0,n-1),x:J2(Math.round(e.x/t),0,r-1)}}function L6(e,t,n,r,a,s,i,o=2){let[l,u]=r.shape,c=tg(t.position,s,l,u),h=gae(e,c,i),p=Q2(t.position,h);for(let A=0;A<o;A++){let y=tg(p,s,l,u),g=Y2(y.y,y.x,n,a);p=Q2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=tg(p,s,l,u),m=r.get(f.y,f.x,n);return{position:p,part:Kl[n],score:m}}function W6(e,t,n,r,a,s){let i=t.shape[2],o=eg.length,l=new Array(i),{part:u,score:c}=e,h=I0(u,r,n);l[u.id]={score:c,part:Kl[u.id],position:h};for(let d=o-1;d>=0;--d){let p=eg[d],f=P6[d];l[p]&&!l[f]&&(l[f]=L6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=P6[d],f=eg[d];l[p]&&!l[f]&&(l[f]=L6(d,l[p],f,t,n,r,a))}return l}async function B6(e,t,n){let r=0,a=O6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],u=D6(l,yae,o),c=await u.buffer(),d=Array.from($6(i,l)).map((f,m)=>(r+=f,{position:{y:c.get(m,0),x:c.get(m,1)},part:Kl[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),u.dispose(),{keypoints:p,score:r/d.length}}var xae=1,V6=16;function U6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return F6(r,n,i.y,i.x)<=t})}function wae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(U6(e,t,s,o)||(a+=i),a),0)/n.length}function j6(e,t,n,r,a,s,i){let o=[],l=R6(i,xae,e),u=a^2;for(;o.length<s&&!l.empty();){let c=l.dequeue(),h=I0(c.part,V6,t);if(U6(o,u,h,c.part.id))continue;let d=W6(c,e,t,V6,n,r),p=wae(o,u,d);p>i&&o.push({keypoints:d,score:p})}return o}async function H6(e){return Promise.all(e.map(t=>t.buffer()))}function 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r=G6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await vae(t,a,n,this.inputSize):await _ae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function sg(e){let t=await ct(e.body.modelPath),n=new X2(t);return e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new ag(n)}var cg={};pr(cg,{HandPose:()=>dg,load:()=>pg});function N0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function jc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function q6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return ze.cropAndResize(t,s,[0],n)}function X6(e,t){let 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g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=H(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let N={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=S0(T0(o),Q6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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n4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],r4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var cr;async function mg(e){return cr||(cr=await ct(e.body.modelPath),cr.width=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[2].size),cr.height=parseInt(cr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),cr}async function Ag(e,t){if(!cr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=ze.resizeBilinear(e,[cr.width,cr.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>cr.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),fn("blazepose",u)}else{let u=await cr.predict(a);s=u.find(c=>c.size===195||c.size===155).dataSync(),u.forEach(c=>c.dispose())}a.dispose();let i=[],o=s.length===195?n4:r4,l=5;for(let 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a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&kg(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s)}}}}async function p4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ie.font;for(let r of t){if(ie.drawBoxes&&(n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]),ie.drawLabels&&(ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+ie.lineHeight,r.box[2])),n.fillStyle=ie.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+ie.lineHeight,r.box[2])),n.stroke()),ie.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=ie.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:ie.color,F0(n,a[0],a[1]);if(ie.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=ie.lineWidth,n.beginPath(),n.strokeStyle=ie.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:ie.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function f4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ie.font;for(let r of t)if(ie.drawBoxes){if(n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]),ie.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+ie.lineHeight,r.box[2])),n.fillStyle=ie.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+ie.lineHeight,r.box[2])}n.stroke()}}}async function $ae(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Dae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(h4(e,t.face),d4(e,t.body),p4(e,t.hand),c4(e,t.gesture),f4(e,t.object))}var D0=`
+`)}var $0=2048,Tt=null,tn=null,Rt=null;function vg(e,t){let n;if(e instanceof Ve)n=Rr(e);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>$0&&(i=$0,o=i*s/a),o>$0&&(o=$0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)return Ne("Human: invalid input",e),{tensor:null,canvas:null};(!Tt||Tt.width!==i||Tt.height!==o)&&(Tt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Tt.width!==i&&(Tt.width=i),Tt.height!==o&&(Tt.height=o));let l=Tt.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Tt.width,Tt.height),t.filter.enabled){if((!Rt||!tn||Tt.width!==tn.width||Tt.height!==tn.height)&&(tn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Tt.width,Tt.height):document.createElement("canvas"),tn.width!==Tt.width&&(tn.width=Tt.width),tn.height!==Tt.height&&(tn.height=Tt.height),Rt=fr.flags.IS_BROWSER?new l4({canvas:tn}):null),!Rt)return{tensor:null,canvas:Tt};Rt.reset(),Rt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Rt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Rt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Rt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Rt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Rt.addFilter("hue",t.filter.hue),t.filter.negative&&Rt.addFilter("negative"),t.filter.sepia&&Rt.addFilter("sepia"),t.filter.vintage&&Rt.addFilter("brownie"),t.filter.sepia&&Rt.addFilter("sepia"),t.filter.kodachrome&&Rt.addFilter("kodachrome"),t.filter.technicolor&&Rt.addFilter("technicolor"),t.filter.polaroid&&Rt.addFilter("polaroid"),t.filter.pixelate!==0&&Rt.addFilter("pixelate",t.filter.pixelate),Rt.apply(Tt)}else tn=Tt,Rt&&(Rt=null);let u;if(tn.data){let h=[tn.height,tn.width,3];u=ld(tn.data,h,"int32")}else if(t.backend==="webgl"||tn instanceof ImageData)u=al.fromPixels(tn);else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(tn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);u=al.fromPixels(p)}let c=u.toFloat();n=c.expandDims(0),u.dispose(),c.dispose()}let r=t.filter.return?tn:null;return{tensor:n,canvas:r}}var kg={};pr(kg,{all:()=>$ae,body:()=>h4,canvas:()=>Fae,drawOptions:()=>ie,face:()=>c4,gesture:()=>u4,hand:()=>d4,object:()=>p4});var pt={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var ie={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function D0(e,t,n,r=null){e.fillStyle=ie.useDepth&&r?`rgba(${127.5+2*(r||0)}, ${127.5-2*(r||0)}, 255, 0.3)`:ie.color,e.beginPath(),e.arc(t,n,ie.pointSize,0,2*Math.PI),e.fill()}function Yl(e,t,n,r,a){if(e.beginPath(),ie.useCurves){let s=(t+t+r)/2,i=(n+n+a)/2;e.ellipse(s,i,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=ie.lineWidth,e.moveTo(t+ie.roundRect,n),e.lineTo(t+r-ie.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+ie.roundRect),e.lineTo(t+r,n+a-ie.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-ie.roundRect,n+a),e.lineTo(t+ie.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-ie.roundRect),e.lineTo(t,n+ie.roundRect),e.quadraticCurveTo(t,n,t+ie.roundRect,n),e.closePath();e.stroke()}function Ig(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=ie.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:ie.color,e.fillStyle=ie.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:ie.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),ie.fillPolygons&&(e.closePath(),e.fill())}}function O0(e,t=[]){if(!(t===void 0||t.length===0)){if(!ie.useCurves||t.length<=2){Ig(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,a=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),ie.fillPolygons&&(e.closePath(),e.fill())}}async function u4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=ie.font,n.fillStyle=ie.color;let r=1;for(let a=0;a<t.length;a++){let s=[],i=[];if([s,i]=Object.entries(t[a]),i.length>1&&i[1].length>0){let o=s[1]>0?`#${s[1]}`:"",l=`${s[0]} ${o}: ${i[1]}`;ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(l,8,2+r*ie.lineHeight)),n.fillStyle=ie.labelColor,n.fillText(l,6,0+r*ie.lineHeight),r+=1}}}async function c4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let r of t){n.font=ie.font,n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.drawBoxes&&(ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]));let a=[];if(a.push(`face confidence: ${Math.trunc(100*r.confidence)}%`),r.genderConfidence&&a.push(`${r.gender||""} ${Math.trunc(100*r.genderConfidence)}% confident`),r.age&&a.push(`age: ${r.age||""}`),r.iris&&a.push(`iris distance: ${r.iris}`),r.emotion&&r.emotion.length>0){let s=r.emotion.map(i=>`${Math.trunc(100*i.score)}% ${i.emotion}`);a.push(s.join(" "))}r.angle&&r.angle.roll&&a.push(`roll: ${Math.trunc(100*r.angle.roll)/100} yaw:${Math.trunc(100*r.angle.yaw)/100} pitch:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=ie.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*ie.lineHeight+r.box[1];ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=ie.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh&&r.mesh.length>0){if(ie.drawPoints)for(let s of r.mesh)D0(n,s[0],s[1],s[2]);if(ie.drawPolygons){n.lineWidth=1;for(let s=0;s<Mi.length/3;s++){let i=[Mi[s*3+0],Mi[s*3+1],Mi[s*3+2]].map(o=>r.mesh[o]);Ig(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=ie.useDepth?"rgba(255, 200, 255, 0.3)":ie.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ie.fillPolygons&&(n.fillStyle=ie.useDepth?"rgba(255, 255, 200, 0.3)":ie.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=ie.useDepth?"rgba(255, 200, 255, 0.3)":ie.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ie.fillPolygons&&(n.fillStyle=ie.useDepth?"rgba(255, 255, 200, 0.3)":ie.color,n.fill())}}}}}var Qa=[];async function h4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!Qa[r]&&ie.bufferedOutput&&(Qa[r]={...t[r]}),n.strokeStyle=ie.color,n.lineWidth=ie.lineWidth,ie.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=ie.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:ie.color,ie.bufferedOutput?(Qa[r].keypoints[a][0]=(Qa[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,Qa[r].keypoints[a][1]=(Qa[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,D0(n,Qa[r].keypoints[a][0],Qa[r].keypoints[a][1])):D0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(ie.drawLabels&&(n.font=ie.font,t[r].keypoints))for(let a of t[r].keypoints)n.fillStyle=ie.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:ie.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4);if(ie.drawPolygons&&t[r].keypoints){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&Ig(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s)}}}}async 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0.5)`:ie.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function p4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ie.font;for(let r of t)if(ie.drawBoxes){if(n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]),ie.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+ie.lineHeight,r.box[2])),n.fillStyle=ie.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+ie.lineHeight,r.box[2])}n.stroke()}}}async function Fae(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function $ae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(c4(e,t.face),h4(e,t.body),d4(e,t.hand),u4(e,t.gesture),p4(e,t.object))}var z0=`
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backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ne("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ne(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ne("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&g6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ne("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ne("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ne(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-n)}});P0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(D0);break;case"full":n=await t(O0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});L0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+D0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+O0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));W0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(D0):t(O0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=vg,this.version=m4,this.config=qi(pt,t),this.state="idle",rs(this,Jl,0),rs(this,Hc,!1),rs(this,Gc,!1),rs(this,$i,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>_g(n,this.config),this.classes={facemesh:j2,age:w2,gender:_2,emotion:S2,faceres:F2,body:this.config.body.modelPath.includes("posenet")?ng:pg,hand:ug,nanodet:gg},this.sysinfo=Pg()}profileData(){return this.config.profile?x2:{}}similarity(t,n){return this.config.face.description.enabled?D2(t,n):this.config.face.embedding.enabled?x6(t,n):0}enhance(t){return O2(t)}match(t,n,r=0){return w6(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=qi(this.config,t)),Jn(this,$i)&&(this.config.debug&&Ne(`version: 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Promise.all([this.models.face||(this.config.face.enabled?G2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?b2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?N2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?E2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?R2(this.config):null),this.models.handpose||(this.config.hand.enabled?dg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?ag(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?fg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Ag(this.config):null),this.models.nanodet||(this.config.object.enabled?wg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?$2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await G2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await b2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await N2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await E2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await R2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await dg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await ag(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await fg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Ag(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await wg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await $2(this.config))),Jn(this,$i)&&(this.config.debug&&Ne("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),rs(this,$i,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var m,A,y,g;this.state="config";let a;this.config=qi(this.config,n),this.state="check";let s=Jn(this,z0).call(this,t);s&&(Ne(s,t),r({error:s}));let i=Ye();await Jn(this,qc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=Ye();let o=_g(t,this.config);if(!o||!o.tensor){Ne("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let l,c,u,h,d;this.config.async?(u=this.config.face.enabled?z2(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),u=this.config.face.enabled?await z2(this,o.tensor):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?mg(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?yg(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await mg(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?await yg(o.tensor,this.config):[]),d=Math.trunc(Ye()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(c=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),c=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?bg(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),h=this.config.object.enabled?await bg(o.tensor,this.config):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([u,l,c,h]=await Promise.all([u,l,c,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(a=Ye(),p=[...i4(u),...s4(l),...l4(c),...o4(u)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let f={face:u,body:l,hand:c,gesture:p,object:h,performance:this.perf,canvas:o.canvas};r(f)})}async warmup(t={}){let n=Ye();t&&(this.config=qi(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await Jn(this,P0).call(this):typeof Image!="undefined"?a=await Jn(this,L0).call(this):a=await Jn(this,W0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&Ne("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};Jl=new WeakMap,Hc=new WeakMap,Gc=new WeakMap,$i=new WeakMap,z0=new WeakMap,qc=new WeakMap,P0=new WeakMap,L0=new WeakMap,W0=new WeakMap;export{Oae as Human,Oae as default};
+2Q==`;var f4="1.2.5";var Jl,Hc,Gc,$i,L0,qc,W0,B0,V0,Dae=class{constructor(t={}){Jl.set(this,void 0);Hc.set(this,void 0);Gc.set(this,void 0);$i.set(this,void 0);this.analyze=(...t)=>{if(!Jn(this,Hc))return;let n=this.tf.engine().state.numTensors,r=Jn(this,Jl);rs(this,Jl,n);let a=n-r;a!==0&&Ne(...t,a)};L0.set(this,t=>{if(!Jn(this,Gc))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Ve))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});qc.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=Ye();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&Ne("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ne("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ne(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ne("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&x6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ne("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ne("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ne(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-n)}});W0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(z0);break;case"full":n=await t(P0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});B0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+z0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+P0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));V0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(z0):t(P0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=kg,this.version=f4,this.config=qi(pt,t),this.state="idle",rs(this,Jl,0),rs(this,Hc,!1),rs(this,Gc,!1),rs(this,$i,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>vg(n,this.config),this.classes={facemesh:H2,age:b2,gender:v2,emotion:T2,faceres:$2,body:this.config.body.modelPath.includes("posenet")?rg:fg,hand:cg,nanodet:xg},this.sysinfo=Lg()}profileData(){return this.config.profile?l0:{}}similarity(t,n){return this.config.face.description.enabled?O2(t,n):this.config.face.embedding.enabled?w6(t,n):0}enhance(t){return z2(t)}match(t,n,r=0){return b6(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=qi(this.config,t)),Jn(this,$i)&&(this.config.debug&&Ne(`version: ${this.version}`),this.config.debug&&Ne(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ne("platform:",this.sysinfo.platform),this.config.debug&&Ne("agent:",this.sysinfo.agent),await Jn(this,qc).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ne("configuration:",this.config),this.config.debug&&Ne("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?q2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?_2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?S2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?R2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?M2(this.config):null),this.models.handpose||(this.config.hand.enabled?pg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?sg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?mg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?yg(this.config):null),this.models.nanodet||(this.config.object.enabled?bg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?D2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await q2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await _2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await S2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await R2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await M2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await pg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await sg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await mg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await yg(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await bg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await D2(this.config))),Jn(this,$i)&&(this.config.debug&&Ne("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),rs(this,$i,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var m,A,y,g;this.state="config";let a;this.config=qi(this.config,n),this.state="check";let s=Jn(this,L0).call(this,t);s&&(Ne(s,t),r({error:s}));let i=Ye();await Jn(this,qc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=Ye();let o=vg(t,this.config);if(!o||!o.tensor){Ne("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let l,u,c,h,d;this.config.async?(c=this.config.face.enabled?P2(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),c=this.config.face.enabled?await P2(this,o.tensor):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?Ag(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?gg(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await Ag(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?await gg(o.tensor,this.config):[]),d=Math.trunc(Ye()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?_g(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),h=this.config.object.enabled?await _g(o.tensor,this.config):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([c,l,u,h]=await Promise.all([c,l,u,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(a=Ye(),p=[...s4(c),...a4(l),...o4(u),...i4(c)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let f={face:c,body:l,hand:u,gesture:p,object:h,performance:this.perf,canvas:o.canvas};r(f)})}async warmup(t={}){let n=Ye();t&&(this.config=qi(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await Jn(this,W0).call(this):typeof Image!="undefined"?a=await Jn(this,B0).call(this):a=await Jn(this,V0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&Ne("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};Jl=new WeakMap,Hc=new WeakMap,Gc=new WeakMap,$i=new WeakMap,L0=new WeakMap,qc=new WeakMap,W0=new WeakMap,B0=new WeakMap,V0=new WeakMap;export{Dae as Human,Dae as default};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index ab4aeb81..7d5f13d1 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
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-  "sourcesContent": ["// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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_ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var 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_OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var 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___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=function(){return(___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=Module[\"asm\"][\"db\"]).apply(null,arguments)};var _emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=function(){return(_emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=Module[\"asm\"][\"eb\"]).apply(null,arguments)};var _emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=function(){return(_emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=Module[\"asm\"][\"fb\"]).apply(null,arguments)};var _emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n  return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return 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Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). 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fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return 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false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(name: string, raw: any): void {\n  if (!raw || !raw.kernels) return;\n  const maxResults = 5;\n  const time = raw.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n  data[name] = res;\n  log('Human profiler', name, res);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    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y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\nconst activateScore = false;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position\n          if (score > config.object.minConfidence) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            let boxRaw = [ // results normalized to range 0..1\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n              cx + (scaleBox / strideSize * boxOffset[2]),\n              cy + (scaleBox / strideSize * boxOffset[3]),\n            ];\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n  const nmsIdx = nms.dataSync();\n  tf.dispose(nms);\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    // @ts-ignore\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    resize.dispose();\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result.clone();\n      profileObject.result.dispose();\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      tf.enableDebugMode();\n      */\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
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+  "sourcesContent": ["// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var 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_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n  return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var 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XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return 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Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    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y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.executeAsync(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      this.tf.enableDebugMode();\n      */\n      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
+  "mappings": 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   "names": []
 }
diff --git a/dist/human.js b/dist/human.js
index 7533d2a9..124aea98 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -5,31 +5,31 @@
   author: <https://github.com/vladmandic>'
   */
 
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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,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(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),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 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=gf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(gf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=td(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=gf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=cf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}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 a=e;n==="string"&&ba(e[0])&&(a=e.map(o=>Ru(o)));let s=r.write(a,t,n),i=new Ve(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Xg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ve(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Du(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*nf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Du||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*nf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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(){let 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;let 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(let r of 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+`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function $u(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var $t=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Ft(e),n!=null){let r=n.length;M(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Gg(t,this.size),this.strides=Ki(e)}set(e,...t){t.length===0&&(t=[0]),M(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}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];let 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 Cr().makeTensor(this.values,this.shape,this.dtype)}},Cr=null,Qo=null,vk=null;function kk(e){Cr=e}function Ik(e){Qo=e}function Nk(e){vk=e}var Ve=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ft(e),this.strides=Ki(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Qo.buffer(this.shape,this.dtype,e)}bufferSync(){return Qo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Zi(this.shape,e)}arraySync(){return Zi(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Cr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>rd(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();let e=Cr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>rd(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();let e=await Cr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Cr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Qo.print(this,e)}clone(){return this.throwIfDisposed(),Qo.clone(this)}toString(e=!1){let t=this.dataSync();return _k(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Qo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cr().makeVariable(this,e,t,n)}};Object.defineProperty(Ve,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return cf("Tensor",()=>Ve)}Z();var Du=class extends Ve{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(!ea(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Cr().disposeTensor(this),this.dataId=e.dataId,Cr().incRef(this,null)}dispose(){Cr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Du,Symbol.hasInstance,{value:e=>e instanceof Ve&&e.assign!=null&&e.assign instanceof Function});var mr={};Oe(mr,{assertTypesMatch:()=>o5,getTensorsInContainer:()=>ff,isTensorInList:()=>Sk,makeTypesMatch:()=>bt});var mf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(mf||(mf={}));var Af;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Af||(Af={}));var yf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(yf||(yf={}));var gf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(gf||(gf={}));var xf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(xf||(xf={}));var Tk={float32:gf,int32:Af,bool:yf,complex64:xf};function nr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Tk[e][t]}function sd(e){return nr(e,"int32")}function bt(e,t){if(e.dtype===t.dtype)return[e,t];let n=nr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function o5(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Sk(e,t){return t.some(n=>n.id===e.id)}function ff(e){let t=[],n=new Set;return l5(e,t,n),t}function l5(e,t,n){if(e==null)return;if(e instanceof Ve){t.push(e);return}if(!Ck(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),l5(s,t,n))}}function Ck(e){return Array.isArray(e)||typeof e=="object"}function wf(e){return e.kernelName!=null}var u5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Ou=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new u5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,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(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),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 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=wf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(wf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=td(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:u,attrs:c}=e,h=wf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=df(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}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 a=e;n==="string"&&ba(e[0])&&(a=e.map(o=>Ru(o)));let s=r.write(a,t,n),i=new Ve(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Kg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ve(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Du(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*af(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Du||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*af(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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(){let 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;let 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(let r of 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n.makeTensorInfo(a.shape,a.dtype,m)}var FM={kernelName:bs,backendName:"cpu",kernelFunc:MM};function $M(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=At({inputs:{x:a},backend:n,attrs:{shape:l}}),f=ir({inputs:{x:p},backend:n,attrs:{perm:c}}),m=At({inputs:{x:f},backend:n,attrs:{shape:u}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var DM={kernelName:fu,backendName:"cpu",kernelFunc:$M};function OM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Em(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var 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mF={kernelName:Fh,backendName:"cpu",kernelFunc:fF};function AF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;ve([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new $t(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,w=n.data.get(a.dataId).values,[b,_,x]=h,N=n.data.get(s.dataId).values,[T,C,F]=d,{batchSize:P,filterHeight:W,filterWidth:V,inChannels:U,inHeight:H,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,oe=W-1-p.padInfo.top,Q=V-1-p.padInfo.left,he=G/U;for(let le=0;le<P;++le)for(let fe=0;fe<U;++fe)for(let pe=0;pe<H;++pe){let ke=pe-oe,Se=Math.max(0,Math.ceil(ke/ae)),Me=Math.min(ee,(W+ke)/ae);for(let De=0;De<X;++De){let Fe=De-Q,Qe=Math.max(0,Math.ceil(Fe/te)),et=Math.min(Y,(V+Fe)/te),st=0;for(let Ke=Se;Ke<Me;++Ke){let ht=Ke*ae-ke;for(let Be=Qe;Be<et;++Be){let 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te=v.locToIndex([U,H,G,Y],W,v.computeStrides(F));V[te]=ae}}}return{dataId:l.write(v.toTypedArray(V,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},bF={kernelName:zh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=v.toNestedArray(r.shape,c.data.get(r.dataId).values),h=v.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:w,strideWidth:b,filterHeight:_,filterWidth:x,dilationHeight:N,dilationWidth:T,outShape:C}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===C.length,()=>`Error in ${zh}, dy must have the same rank as output ${C.length}, but got ${s.rank}`);let F=v.toNestedArray(C,c.data.get(s.dataId).values),P=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let W=0;W<d;++W)for(let V=0;V<A;++V){let U=V*w-g.top;for(let H=0;H<y;++H){let X=H*b-g.left;for(let G=0;G<m;++G){let 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n.makeTensorInfo(a.shape,a.dtype,m)}var MM={kernelName:bs,backendName:"cpu",kernelFunc:RM};function FM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=At({inputs:{x:a},backend:n,attrs:{shape:l}}),f=ir({inputs:{x:p},backend:n,attrs:{perm:u}}),m=At({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var $M={kernelName:fu,backendName:"cpu",kernelFunc:FM};function DM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Mm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var 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r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function QO(e,t){let n=Xm(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function ob(e){return 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Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function hn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
+`)[0]),console.log(`%c ${v.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
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e.getUniformLocation(t,n)}function eb(e,t,n,r){we(e,()=>Yw(e,t,r)),we(e,()=>e.uniform1i(n,r))}function WO(e){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),we(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function rp(e,t,n){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function qm(e,t){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function oc(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+tb(e,t))}function tb(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function oa(e,t,n){let r=we(e,()=>t());if(r==null)throw new Error(n);return r}function ub(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function yi(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function gi(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function ap(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[yi(e),...gi(e)]),t}function nb(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?v.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let 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ab(e){if(lp==null){let t=Wr(e);lp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,lp)}function sb(e){if(e===0)return 0;let t,n=Wr(e);return qn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:qn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function qn(e,t){return e.getExtension(t)!=null}function Xm(e){try{if(Wr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function ib(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!qn(t,"OES_texture_float"))return!1}else if(!qn(t,"EXT_color_buffer_float"))return!1;return Ym(t)}function ob(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!qn(t,"OES_texture_float")||!qn(t,"WEBGL_color_buffer_float"))return!1}else{if(qn(t,"EXT_color_buffer_float"))return Ym(t);let n="EXT_color_buffer_half_float";if(qn(t,n)){let r=t.getExtension(n);return JO(t,r)}return!1}return Ym(t)}function Ym(e){let t=Zm(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function JO(e,t){let n=Zm(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function lb(e){return e!==2?!1:Wr(e).fenceSync!=null}function kl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Re=J();Re.registerFlag("HAS_WEBGL",()=>Re.getNumber("WEBGL_VERSION")>0);Re.registerFlag("WEBGL_VERSION",()=>Xm(2)?2:Xm(1)?1:0);Re.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Re.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Re.get("WEBGL_VERSION")===2);Re.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Re.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Re.registerFlag("WEBGL_PACK",()=>Re.getBool("HAS_WEBGL"));Re.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_CLIP",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Re.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_REDUCE",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_LAZILY_UNPACK",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_CONV_IM2COL",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>rb(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>ab(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Re.getNumber("WEBGL_VERSION");return e===0?0:sb(e)});Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Re.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!zu.isMobile());Re.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>ib(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Re.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Re.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Re.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>ob(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_FENCE_API_ENABLED",()=>lb(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Re.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Re.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Re.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>zu.isMobile()&&Re.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function hn(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -40,7 +40,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       }
 
       #define isnan(value) isnan_custom(value)
-    `,l="",c=`
+    `,l="",u=`
       #define round(value) newRound(value)
       int newRound(float value) {
         return int(floor(value + 0.5));
@@ -66,7 +66,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isinf(vec4 val) {
         return equal(abs(val), vec4(INFINITY));
       }
-    `,c=`
+    `,u=`
       int round(float value) {
         return int(floor(value + 0.5));
       }
@@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function wi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Zm(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function wi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Jm(e){let t=v.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var ub=`
+`}var cb=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -117,7 +117,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,ez=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
+`,QO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${wi(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -138,7 +138,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},tz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
+    `}},ez=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=uc.DENSE;let t=hc(e),n=hn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
         ${wi(["r","c","d"],e)}
         return ivec3(r, c, d);
@@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},nz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
-      ${ub}
+    `}},tz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
+      ${cb}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},rz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
-      ${ub}
+    `}},nz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Xn.DOWNLOAD;let t=hn();this.outputShape=e,this.userCode=`
+      ${cb}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},az=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=hn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${Zm(e)}
+    `}},rz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=hn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${Jm(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -204,10 +204,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},sz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=hn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
+    `}},az=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=hn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=`
           localCoords = coords;
-          if(localCoords[2] + ${c} < ${e[2]}) {
-            localCoords[2] += ${c};
+          if(localCoords[2] + ${u} < ${e[2]}) {
+            localCoords[2] += ${u};
             if(localCoords[1] + ${l} < ${e[1]}) {
               localCoords[1] += ${l};
 
@@ -222,18 +222,18 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
               values = ${r.texture2D}(A, uv);
 
               if(offset == 0) {
-                result[${u}] = values[0];
+                result[${c}] = values[0];
               } else if(offset == 1) {
-                result[${u}] = values[1];
+                result[${c}] = values[1];
               } else if(offset == 2) {
-                result[${u}] = values[2];
+                result[${c}] = values[2];
               } else {
-                result[${u}] = values[3];
+                result[${c}] = values[3];
               }
             }
           }
         `}this.userCode=`
-      ${Zm(e)}
+      ${Jm(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = ${o};
       }
-    `}},cb={};Oe(cb,{bindVertexProgramAttributeStreams:()=>xb,createBufferFromOutputTexture:()=>_b,createFloat16MatrixTexture:()=>mb,createFloat16PackedMatrixTexture:()=>gb,createFloat32MatrixTexture:()=>fb,createIndexBuffer:()=>pb,createPackedMatrixTexture:()=>yb,createUnsignedBytesMatrixTexture:()=>Ab,createVertexBuffer:()=>db,createVertexShader:()=>hb,downloadByteEncodedFloatMatrixFromOutputTexture:()=>kb,downloadFloat32MatrixFromBuffer:()=>vb,downloadMatrixFromPackedOutputTexture:()=>Nb,downloadPackedMatrixFromBuffer:()=>Ib,getInternalFormatForFloat16MatrixTexture:()=>Jm,getInternalFormatForFloat16PackedMatrixTexture:()=>tA,getInternalFormatForFloat32MatrixTexture:()=>Ym,getInternalFormatForPackedMatrixTexture:()=>eA,getInternalFormatForUnsignedBytesMatrixTexture:()=>Qm,uploadDenseMatrixToTexture:()=>wb,uploadPixelDataToTexture:()=>bb});function hb(e){let t=hn(),n=`${t.version}
+    `}},hb={};Oe(hb,{bindVertexProgramAttributeStreams:()=>wb,createBufferFromOutputTexture:()=>vb,createFloat16MatrixTexture:()=>Ab,createFloat16PackedMatrixTexture:()=>xb,createFloat32MatrixTexture:()=>mb,createIndexBuffer:()=>fb,createPackedMatrixTexture:()=>gb,createUnsignedBytesMatrixTexture:()=>yb,createVertexBuffer:()=>pb,createVertexShader:()=>db,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Ib,downloadFloat32MatrixFromBuffer:()=>kb,downloadMatrixFromPackedOutputTexture:()=>Sb,downloadPackedMatrixFromBuffer:()=>Nb,getInternalFormatForFloat16MatrixTexture:()=>eA,getInternalFormatForFloat16PackedMatrixTexture:()=>rA,getInternalFormatForFloat32MatrixTexture:()=>Qm,getInternalFormatForPackedMatrixTexture:()=>nA,getInternalFormatForUnsignedBytesMatrixTexture:()=>tA,uploadDenseMatrixToTexture:()=>bb,uploadPixelDataToTexture:()=>_b});function db(e){let t=hn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return Bw(e,n)}function db(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Hw(e,t)}function pb(e){let t=new Uint16Array([0,1,2,2,1,3]);return Gw(e,t)}function dc(e,t,n,r,a,s){Xw(t,n);let i=qw(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Ym(e){return e.internalFormatFloat}function fb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Ym(r),r.textureFormatFloat,e.FLOAT)}function Jm(e){return e.internalFormatHalfFloat}function mb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Jm(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function Qm(e){return e.downloadTextureFormat}function Ab(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Qm(r),e.RGBA,e.UNSIGNED_BYTE)}function eA(e){return e.internalFormatPackedFloat}function yb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,eA(r),e.RGBA,e.FLOAT)}function tA(e){return e.internalFormatPackedHalfFloat}function gb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,tA(r),e.RGBA,r.textureTypeHalfFloat)}function xb(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),jm(e,t,"clipSpacePos",n,3,s,r)&&jm(e,t,"uv",n,2,s,a)}function wb(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function bb(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _b(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function vb(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function kb(e,t,n,r){let[a,s]=cc(t,n),i=4,o=new Uint8Array(GO(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Ib(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(qO(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function Nb(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var up=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ic(this.gl,a),qn(this.gl,s))this.textureHalfFloatExtension=ic(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),qn(this.gl,r))this.colorBufferHalfFloatExtension=ic(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",qn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(qn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=db(this.gl),this.indexBuffer=pb(this.gl),this.framebuffer=Kw(this.gl),this.textureConfig=Xm(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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r=this.gl;rp(r,e,this.framebuffer),this.debug&&oc(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function iz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Sb}=R;function mz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
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+    }`;return Vw(e,n)}function pb(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Gw(e,t)}function fb(e){let t=new Uint16Array([0,1,2,2,1,3]);return qw(e,t)}function dc(e,t,n,r,a,s){Kw(t,n);let i=Xw(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Qm(e){return e.internalFormatFloat}function mb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,Qm(r),r.textureFormatFloat,e.FLOAT)}function eA(e){return e.internalFormatHalfFloat}function Ab(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,eA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function tA(e){return e.downloadTextureFormat}function yb(e,t,n,r){let[a,s]=cc(t,n);return dc(e,a,s,tA(r),e.RGBA,e.UNSIGNED_BYTE)}function nA(e){return e.internalFormatPackedFloat}function gb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,nA(r),e.RGBA,e.FLOAT)}function rA(e){return e.internalFormatPackedHalfFloat}function xb(e,t,n,r){let[a,s]=Il(t,n);return dc(e,a,s,rA(r),e.RGBA,r.textureTypeHalfFloat)}function wb(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Gm(e,t,"clipSpacePos",n,3,s,r)&&Gm(e,t,"uv",n,2,s,a)}function bb(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _b(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function vb(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function kb(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function Ib(e,t,n,r){let[a,s]=cc(t,n),i=4,o=new Uint8Array(HO(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Nb(e,t,n,r,a,s,i,o){let l=e,u=new Float32Array(GO(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Sb(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var up=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ic(this.gl,a),qn(this.gl,s))this.textureHalfFloatExtension=ic(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),qn(this.gl,r))this.colorBufferHalfFloatExtension=ic(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",qn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(qn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=pb(this.gl),this.indexBuffer=fb(this.gl),this.framebuffer=Zw(this.gl),this.textureConfig=Zm(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),xb(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),gb(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(qm(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Ib(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Nb(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kb(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=vb(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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we(t,()=>t.attachShader(a,r)),we(t,()=>t.attachShader(a,n)),Hw(t,a),this.debug&&np(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=wb(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&np(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Jw(this.gl,e,t):Qw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),eb(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=Il(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&np(this.gl,this.program),oc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ic(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=sz(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),rp(this.gl,e,this.framebuffer),this.debug&&oc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rp(this.gl,this.outputTexture,this.framebuffer),this.debug&&oc(this.gl)):qm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;rp(r,e,this.framebuffer),this.debug&&oc(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function sz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Tb}=R;function fz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
+`),i=e.map(p=>iz(p,t,r)).join(`
+`),o=t.texShape,l=hn(),u=uz(l),c,h,d=dz(l);return t.isPacked?(c=oz(t.logicalShape,o),h=hz(l)):(c=lz(t.logicalShape,o),h=cz(l)),r&&(d+=pz),[d,u,h,s,c,i,n].join(`
+`)}function Nl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return mz(e);case 1:return Az(e);case 2:return yz(e);case 3:return gz(e);case 4:return xz(e);case 5:return wz(e);case 6:return bz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Cb(e){switch(e.shapeInfo.logicalShape.length){case 0:return _z(e);case 1:return vz(e);case 2:return kz(e);case 3:return Iz(e);default:return Nz(e)}}function iz(e,t,n=!1){let r="";n?r+=Cb(e):r+=Nl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Sz(e,t):r+=Tz(e,t)),r}function oz(e,t){switch(e.length){case 0:return Eb();case 1:return Cz(e,t);case 2:return Mz(e,t);case 3:return Ez(e,t);default:return Rz(e,t)}}function lz(e,t){switch(e.length){case 0:return Eb();case 1:return Fz(e,t);case 2:return Pz(e,t);case 3:return $z(e,t);case 4:return Dz(e,t);case 5:return Oz(e,t);case 6:return zz(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function uz(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function hz(e){return`
+  `}function cz(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function dz(e){return`
+  `}function hz(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function pz(e){return`${e.version}
+  `}function dz(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
+    ${Lz}
     ${Wz}
     ${Bz}
-    ${Vz}
-  `}var Wz=`
+  `}var Lz=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Bz=`
+`,Wz=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Vz=`
+`,Bz=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,fz=`
+`,pz=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function Cb(){return`
+`;function Eb(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function Ez(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function Cz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${n[1]}.0);
       }
@@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function $z(e,t){return t[0]===1?`
+  `}function Fz(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function Rz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
+  `}function Ez(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function Dz(e,t){let n=wi(["r","c","d"],e);return`
+  `}function $z(e,t){let n=wi(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function Mz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function Rz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function Oz(e,t){let n=wi(["r","c","d","d2"],e);return`
+  `}function Dz(e,t){let n=wi(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function zz(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
+  `}function Oz(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function Pz(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
+  `}function zz(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -474,7 +474,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function Fz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
+  `}function Mz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -489,7 +489,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function Lz(e,t){return v.arraysEqual(e,t)?`
+  `}function Pz(e,t){return v.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -516,11 +516,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function bi(e){return`offset${e}`}function vz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=hn();return`
+  `}function bi(e){return`offset${e}`}function _z(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=hn();return`
     vec4 ${n}() {
       return ${r.texture2D}(${t}, halfCR);
     }
-  `}function Az(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function mz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
@@ -529,13 +529,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function kz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=hn();return`
+  `}function vz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=hn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${a[0]}, ${a[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function yz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function Az(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
         ${Sl(e)}
       }
@@ -558,18 +558,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function Iz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=hn();if(a!=null&&v.arraysEqual(t,a))return`
+  `}function kz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=hn();if(a!=null&&v.arraysEqual(t,a))return`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
         return ${o.texture2D}(${n}, uv);
       }
-    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
+    `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(t[1]/2);return`
     vec4 ${r}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
+      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function gz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
+  `}function yz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
     float ${r}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
       return sampleTexture(${n}, uv);
@@ -584,37 +584,37 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
         ${Sl(e)}
       }
-    `;let l=a[0],c=a[1],u=bi(n);return c===1?`
+    `;let l=a[0],u=a[1],c=bi(n);return u===1?`
     float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
+      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
       return sampleTexture(${n}, uv);
     }
   `:l===1?`
     float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
+      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
       return sampleTexture(${n}, uv);
     }
   `:`
   float ${r}(int row, int col) {
     // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${t[1]} + col + ${u};
-    vec2 uv = uvFromFlat(${l}, ${c}, index);
+    int index = row * ${t[1]} + col + ${c};
+    vec2 uv = uvFromFlat(${l}, ${u}, index);
     return sampleTexture(${n}, uv);
   }
-`}function Nz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Tl(e,h),f=["b","row","col"];return`
-        ${Tb(p)}
+`}function Iz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Tl(e,h),f=["b","row","col"];return`
+        ${Cb(p)}
         vec4 ${r}(int b, int row, int col) {
           return ${r}(${Cl(f,d)});
         }
-      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=hn();return`
+      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=hn();return`
     vec4 ${r}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
-        ${i}, ${o}, ${c}, ${l}, b, row, col);
-      return ${u.texture2D}(${n}, uv);
+        ${i}, ${o}, ${u}, ${l}, b, row, col);
+      return ${c.texture2D}(${n}, uv);
     }
-  `}function xz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Tl(e,l),m=["row","col","depth"];return`
+  `}function gz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Tl(e,l),m=["row","col","depth"];return`
         ${Nl(f)}
         float ${r}(int row, int col, int depth) {
           return ${r}(${Cl(m,o)});
@@ -625,29 +625,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                           vec3(${a}, ${s}, 1)));
         ${Sl(e)}
       }
-    `;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
+    `;let u=e.shapeInfo.texShape,c=u[0],h=u[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
         float ${r}(int row, int col, int depth) {
           float texR = float(row);
           float texC = dot(vec2(col, depth), vec2(${s}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${h}.0, ${u}.0);
+                     vec2(${h}.0, ${c}.0);
           return sampleTexture(${n}, uv);
         }
       `;if(h===s&&d==null)return`
     float ${r}(int row, int col, int depth) {
       float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
       float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${c}.0);
       return sampleTexture(${n}, uv);
     }
   `;let p=bi(n);return`
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${a} + col * ${s} + depth + ${p};
-        vec2 uv = uvFromFlat(${u}, ${h}, index);
+        vec2 uv = uvFromFlat(${c}, ${h}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function Sz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=hn();return`
+  `}function Nz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,c*=t[n-f-1],d=`b${f} * ${c} + `+d;let p=hn();return`
     vec4 ${a}(${h}) {
       int index = ${d};
       int texR = index / ${l};
@@ -655,7 +655,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${p.texture2D}(${r}, uv);
     }
-  `}function wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Tl(e,o),m=["row","col","depth","depth2"];return`
+  `}function xz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Tl(e,o),m=["row","col","depth","depth2"];return`
       ${Nl(f)}
       float ${r}(int row, int col, int depth, int depth2) {
         return ${r}(${Cl(m,l)});
@@ -666,7 +666,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                           vec4(${i}, ${s}, ${a}, 1)));
         ${Sl(e)}
       }
-    `;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
+    `;let u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],d=c[1];if(d===i&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
         float texR = float(row);
         float texC =
@@ -676,7 +676,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                    vec2(${d}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(d===a&&c==null)return`
+    `;if(d===a&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
         float texR = dot(vec3(row, col, depth),
                          vec3(${t[1]*t[2]}, ${t[2]}, 1));
@@ -693,10 +693,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
       return sampleTexture(${n}, uv);
     }
-  `}function bz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=Tl(e,l),A=["row","col","depth","depth2","depth3"];return`
+  `}function wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=Tl(e,l),A=["row","col","depth","depth2","depth3"];return`
       ${Nl(m)}
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${Cl(A,c)});
+        return ${r}(${Cl(A,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -706,7 +706,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           depth3;
         ${Sl(e)}
       }
-    `;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
+    `;let c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
@@ -715,7 +715,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                    vec2(${p}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;if(p===a&&u==null)return`
+    `;if(p===a&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
         float texR = dot(
           vec4(row, col, depth, depth2),
@@ -734,29 +734,29 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${p}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function _z(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Tl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
+  `}function bz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Tl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
       ${Nl(A)}
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         return ${r}(${Cl(y,s)});
       }
-    `}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
+    `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${u}, ${c}, ${l}, ${o})) +
+          vec4(${c}, ${u}, ${l}, ${o})) +
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
         ${Sl(e)}
       }
-    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
+    `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===c&&h==null)return`
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${c}, ${l}, ${o}, ${i})) +
+          vec4(${u}, ${l}, ${o}, ${i})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${f}.0, ${p}.0);
@@ -779,7 +779,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${u} + col * ${c} + depth * ${l} +
+      int index = row * ${c} + col * ${u} + depth * ${l} +
           depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
       vec2 uv = uvFromFlat(${p}, ${f}, index);
       return sampleTexture(${n}, uv);
@@ -790,8 +790,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function Tz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Sb(e.shapeInfo.logicalShape,t.logicalShape),l=ot(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
-`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
+  `}function Sz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Tb(e.shapeInfo.logicalShape,t.logicalShape),l=ot(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(`
+`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?p=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
@@ -800,26 +800,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       `;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
     vec4 ${a}() {
       ${l} coords = getOutputCoords();
-      ${u}
+      ${c}
       vec4 outputValue = get${r}(${d});
       ${p}
     }
-  `}function Cz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
+  `}function Tz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let c=ot(l),u=Sb(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
+    `;let u=ot(l),c=Tb(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${p[m+h]} = 0;`).join(`
 `);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
     float ${a}() {
-      ${c} coords = getOutputCoords();
+      ${u} coords = getOutputCoords();
       ${d}
       return get${r}(${f});
     }
-  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Tl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Cl(e,t){return t.map(n=>e[n]).join(", ")}function Uz(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=mz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function Eb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function jz(e,t,n,r,a){Eb(t.inShapeInfos,n),Eb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function Hz(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:Gz,bincountImpl:Rb,bincountReduceImpl:qz,ceilImpl:Xz,concatImpl:Kz,expImpl:Zz,expm1Impl:Yz,floorImpl:Jz,gatherV2Impl:Qz,greaterImpl:eP,lessImpl:tP,linSpaceImpl:nP,logImpl:rP,maxImpl:aP,maximumImpl:sP,minimumImpl:iP,multiplyImpl:oP,negImpl:lP,prodImpl:uP,rangeImpl:cP,rsqrtImpl:hP,simpleAbsImpl:Mb,sliceImpl:dP,stridedSliceImpl:pP,subImpl:fP,tileImpl:mP,topKImpl:AP,transposeImpl:nA,uniqueImpl:yP}=Cm;function Fb(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function dn(e,t){return t===1?[e]:Fb(e,t)}function gP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var _P=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
+  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Tl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Cl(e,t){return t.map(n=>e[n]).join(", ")}function Vz(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=fz(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(u,f,m),d[`offset${f}`]=e.getUniformLocation(u,`offset${f}`,m)}return{program:t,source:l,webGLProgram:u,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:c,nanLoc:h}}function Rb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Uz(e,t,n,r,a){Rb(t.inShapeInfos,n),Rb([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],c=t.uniformLocations[u],h=t.uniformLocations[`offset${u}`];if(c!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(c,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(c,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,c,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function jz(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:Hz,bincountImpl:Mb,bincountReduceImpl:Gz,ceilImpl:qz,concatImpl:Xz,expImpl:Kz,expm1Impl:Zz,floorImpl:Yz,gatherV2Impl:Jz,greaterImpl:Qz,lessImpl:eP,linSpaceImpl:tP,logImpl:nP,maxImpl:rP,maximumImpl:aP,minimumImpl:sP,multiplyImpl:iP,negImpl:oP,prodImpl:lP,rangeImpl:uP,rsqrtImpl:cP,simpleAbsImpl:Fb,sliceImpl:hP,stridedSliceImpl:dP,subImpl:pP,tileImpl:fP,topKImpl:mP,transposeImpl:aA,uniqueImpl:AP}=Rm;function $b(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function dn(e,t){return t===1?[e]:$b(e,t)}function yP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var bP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let n=dn("rc",t),r=ot(t),a=xP(t,e,n),s=wP(t,e[e.length-1],e[e.length-2],n),i=bP(e,n);this.userCode=`
+      `;else{let n=dn("rc",t),r=ot(t),a=gP(t,e,n),s=xP(t,e[e.length-1],e[e.length-2],n),i=wP(e,n);this.userCode=`
         void main() {
           ${r} rc = getOutputCoords();
 
@@ -831,7 +831,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function vP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function xP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function wP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
+      `}}};function _P(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function gP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function xP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
     int r = ${a[0]};
     int c = ${a[1]};
     int rp1 = r + 1;
@@ -839,12 +839,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function bP(e,t){let n=e.length,r=vP(n,t);return n===1?`getA(rc),
+  `}function wP(e,t){let n=e.length,r=_P(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${r[0]}),
           cEdge ? 0. : getA(${r[1]}),
           rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}var $b=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${r[3]})`}var Db=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
         ${a}
         ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${r>0?"}":""}
       `}this.userCode=`
-      ${kP(t)}
-      ${Zm(e)}
+      ${vP(t)}
+      ${Jm(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -872,12 +872,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function kP(e){return`
+    `}};function vP(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
       ${wi(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var IP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Ob(t,n),a=zb(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Db(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Qt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Qt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Qt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Ob(n,r),s=zb(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Db(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function NP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Db(e,t,n,r,a){let s=SP(t,r),i;if(a){let[l,c]=Il(e[0],e[1]);i=l*c}else{let[l,c]=cc(e[0],e[1]);i=l*c}let o=NP(n,s);return i*o}function SP(e,t){switch(e){case Qt.PACKED_2X2_FLOAT32:return eA(t);case Qt.PACKED_2X2_FLOAT16:return tA(t);case Qt.UNPACKED_FLOAT32:return Ym(t);case Qt.UNPACKED_FLOAT16:return Jm(t);case Qt.PACKED_4X1_UNSIGNED_BYTE:return Qm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function TP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Qt.PACKED_2X2_FLOAT32:Qt.UNPACKED_FLOAT32:e?Qt.PACKED_2X2_FLOAT16:Qt.UNPACKED_FLOAT16}function Ob(e,t){if(e===Xn.UPLOAD)return Qt.PACKED_2X2_FLOAT32;if(e===Xn.RENDER||e==null)return TP(t);if(e===Xn.DOWNLOAD||e===Xn.PIXELS)return Qt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function zb(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var La=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var kP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=zb(t,n),a=Pb(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Ob(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Qt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Qt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Qt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Qt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=zb(n,r),s=Pb(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Ob(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function IP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Ob(e,t,n,r,a){let s=NP(t,r),i;if(a){let[l,u]=Il(e[0],e[1]);i=l*u}else{let[l,u]=cc(e[0],e[1]);i=l*u}let o=IP(n,s);return i*o}function NP(e,t){switch(e){case Qt.PACKED_2X2_FLOAT32:return nA(t);case Qt.PACKED_2X2_FLOAT16:return rA(t);case Qt.UNPACKED_FLOAT32:return Qm(t);case Qt.UNPACKED_FLOAT16:return eA(t);case Qt.PACKED_4X1_UNSIGNED_BYTE:return tA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Qt.PACKED_2X2_FLOAT32:Qt.UNPACKED_FLOAT32:e?Qt.PACKED_2X2_FLOAT16:Qt.UNPACKED_FLOAT16}function zb(e,t){if(e===Xn.UPLOAD)return Qt.PACKED_2X2_FLOAT32;if(e===Xn.RENDER||e==null)return SP(t);if(e===Xn.DOWNLOAD||e===Xn.PIXELS)return Qt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Pb(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var La=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},gr="if (isnan(x)) return x;",CP="return x;",Pb="return abs(x);",EP="return (x >= 0.0) ? x : (exp(x) - 1.0);",RP=gr+`
+    `}},gr="if (isnan(x)) return x;",TP="return x;",Lb="return abs(x);",CP="return (x >= 0.0) ? x : (exp(x) - 1.0);",EP=gr+`
   return (x < 0.0) ? 0.0 : x;
-`,MP=gr+`
+`,RP=gr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,cp="return x;",FP="return x;",$P=`
+`,cp="return x;",MP="return x;",FP=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,DP=`
+`,$P=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,OP=`
+`,DP=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -932,14 +932,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},zP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=dn("rc",t),r=ot(t),a=gP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},OP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=dn("rc",t),r=ot(t),a=yP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${r} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},PP=Pr.whereImpl,LP=1e-7,WP=1e-4,rA={};function BP(e){return e in rA||(rA[e]={}),rA[e]}var VP=128,UP=600;function jP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*UP/1024/1024}var Rl=class extends uu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(J().getNumber("WEBGL_VERSION"));this.binaryCache=BP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new up(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new IP(this.gpgpu),this.numMBBeforeWarning=jP(),this.texData=new xh(this,Mr())}nextDataId(){return Rl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Xn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Xn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new El(i,cp):h=new La(i,cp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=R.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new El(r,cp):p=new La(r,cp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...hc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Mr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Lw(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...hc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?ap(t):t,o=s?new rz(i):new nz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Mr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=VP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return PP(e.shape,t)}packedUnaryOp(e,t,n){let r=new El(e.shape,t),a=this.compileAndRun(r,[e],n);return Mr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Mb(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Pb,e.dtype);let t=new La(e.shape,Pb),n=this.compileAndRun(t,[e]);return Mr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Mr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new zP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new _P(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...gi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[yi(t),...gi(t)],s=new $b(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=ap(r),i;n?i=new tz(s):i=new ez(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===uc.DENSE){let m=hc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!lc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=Hz(e,l,c),h=this.getAndSaveBinary(u,()=>Uz(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),jz(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ge(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?LP:WP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=tb(n,o),t.texShape=u),a!=null){let h=ap(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=Il(u[0],u[1]),d=new sz(h,[f,p],m)):d=new az(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Xn.PIXELS:this.texData.get(A.dataId).usage=Xn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=HP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Rl.nextDataId=0;function HP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var Lb="3.3.0";function Wb(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}zu.isBrowser()&&il("webgl",()=>new Rl,2);var GP={forceHalfFloat:Wb},Bb=`
+    `}},zP=Pr.whereImpl,PP=1e-7,LP=1e-4,sA={};function WP(e){return e in sA||(sA[e]={}),sA[e]}var BP=128,VP=600;function UP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*VP/1024/1024}var Rl=class extends uu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(J().getNumber("WEBGL_VERSION"));this.binaryCache=WP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new up(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new kP(this.gpgpu),this.numMBBeforeWarning=UP(),this.texData=new xh(this,Mr())}nextDataId(){return Rl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Xn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Xn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new El(i,cp):h=new La(i,cp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=R.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new El(r,cp):p=new La(r,cp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...hc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Mr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Ww(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...hc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?ap(t):t,o=s?new nz(i):new tz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Mr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=BP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return zP(e.shape,t)}packedUnaryOp(e,t,n){let r=new El(e.shape,t),a=this.compileAndRun(r,[e],n);return Mr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Fb(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Lb,e.dtype);let t=new La(e.shape,Lb),n=this.compileAndRun(t,[e]);return Mr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Mr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new OP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new bP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...gi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[yi(t),...gi(t)],s=new Db(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=ap(r),i;n?i=new ez(s):i=new QO(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===uc.DENSE){let m=hc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!lc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=jz(e,l,u),h=this.getAndSaveBinary(c,()=>Vz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),Uz(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ge(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?PP:LP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=nb(n,o),t.texShape=c),a!=null){let h=ap(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=Il(c[0],c[1]),d=new az(h,[f,p],m)):d=new rz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Xn.PIXELS:this.texData.get(A.dataId).usage=Xn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=jP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};Rl.nextDataId=0;function jP(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var Wb="3.3.0";function Bb(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}zu.isBrowser()&&il("webgl",()=>new Rl,2);var HP={forceHalfFloat:Bb},Vb=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,Ml=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
@@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function On(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var qP={kernelName:vs,backendName:"webgl",kernelFunc:On};function Wa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=On({inputs:{x:r},backend:n}),l=On({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var XP={kernelName:Th,backendName:"webgl",kernelFunc:Wa},Vb="return (a < 0.) ? b * a : a;",Ub=`
+    `}};function On(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var GP={kernelName:vs,backendName:"webgl",kernelFunc:On};function Wa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=On({inputs:{x:r},backend:n}),l=On({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var qP={kernelName:Th,backendName:"webgl",kernelFunc:Wa},Ub="return (a < 0.) ? b * a : a;",jb=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function KP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(Ub,a.shape,i.shape):new Ml(Vb,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var ZP={kernelName:ks,backendName:"webgl",kernelFunc:KP},jb="return (a < 0.) ? b * a : a;",Hb=`
+`;function XP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(jb,a.shape,i.shape):new Ml(Ub,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var KP={kernelName:ks,backendName:"webgl",kernelFunc:XP},Hb="return (a < 0.) ? b * a : a;",Gb=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function YP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(Hb,r.shape,a.shape):new Ml(jb,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var JP={kernelName:Os,backendName:"webgl",kernelFunc:YP},Gb="if (isnan(x)) return x;",QP=`
+`;function ZP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(Gb,r.shape,a.shape):new Ml(Hb,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var YP={kernelName:Os,backendName:"webgl",kernelFunc:ZP},qb="if (isnan(x)) return x;",JP=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,eL=`
+`,QP=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new El(i.shape,t):u=new La(i.shape,e),o.runWebGLProgram(u,[i],l)}}function en({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:c.shape},T=new Ml(e,l.shape,c.shape);return u.runWebGLProgram(T,[x,N],nr(b.dtype,_.dtype))}),g=Wa({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||nr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new pc(t,l.shape,c.shape,n):p=new Ml(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function dp(e,t=!1){if(e==="linear")return t?FP:CP;if(e==="relu")return t?DP:RP;if(e==="elu")return t?$P:EP;if(e==="relu6")return t?OP:MP;if(e==="prelu")return t?Hb:jb;if(e==="leakyrelu")return t?Ub:Vb;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var qb=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new El(i.shape,t):c=new La(i.shape,e),o.runWebGLProgram(c,[i],l)}}function en({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:u.shape},T=new Ml(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],nr(b.dtype,_.dtype))}),g=Wa({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||nr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),w=c.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new pc(t,l.shape,u.shape,n):p=new Ml(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function dp(e,t=!1){if(e==="linear")return t?MP:TP;if(e==="relu")return t?$P:EP;if(e==="elu")return t?FP:CP;if(e==="relu6")return t?DP:RP;if(e==="prelu")return t?Gb:Hb;if(e==="leakyrelu")return t?jb:Ub;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Xb=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1014,11 +1014,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${m}
 
-      const float sharedDimension = ${u}.0;
+      const float sharedDimension = ${c}.0;
 
       vec4 dot2x2ARowBCol(ivec3 rc) {
         vec4 result = vec4(0);
-        for (int i = 0; i < ${u}; i++) {
+        for (int i = 0; i < ${c}; i++) {
           int batchA = ${g};
           int batchB = ${w};
           vec4 a = getMatrixA(batchA, ${h});
@@ -1042,7 +1042,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},Xb={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Kb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},Kb={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Zb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},Zb="return a * b;";function Yb(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new Kb(Xb.REAL,r.shape,a.shape),u=new Kb(Xb.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=oP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new pc(Zb,r.shape,a.shape):i=new Ml(Zb,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var tL={kernelName:Ms,backendName:"webgl",kernelFunc:Yb};function nL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new $b(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),c=v.sizeFromShape(l);v.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!lc(a.shape,l)&&!(u.texture!==null&&lc(u.shape,l))?nL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var rL={kernelName:Oo,backendName:"webgl",kernelFunc:xe},Jb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
+    `}},Yb="return a * b;";function Jb(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new Zb(Kb.REAL,r.shape,a.shape),c=new Zb(Kb.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=iP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new pc(Yb,r.shape,a.shape):i=new Ml(Yb,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var eL={kernelName:Ms,backendName:"webgl",kernelFunc:Jb};function tL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new Db(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!lc(a.shape,l)&&!(c.texture!==null&&lc(c.shape,l))?tL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var nL={kernelName:Oo,backendName:"webgl",kernelFunc:xe},Qb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1063,7 +1063,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${c}
+        ${u}
         return getX(batch, inIdx);
       }
 
@@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},aL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
+    `}},rL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1150,7 +1150,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float allValue = 1.0;
         float anyValue = 0.0;
 
-        for (int i = 0; i < ${c}; i += 4) {
+        for (int i = 0; i < ${u}; i += 4) {
           int inIdx = inOffset + i;
           ${d} values = ${d}(
             getValue(batch, inIdx),
@@ -1162,8 +1162,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${h}
         }
 
-        int inIdx = inOffset + ${c};
-        if (${u===1}) {
+        int inIdx = inOffset + ${u};
+        if (${c===1}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             initializationValue,
@@ -1172,7 +1172,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           );
 
           ${h}
-        } else if (${u===2}) {
+        } else if (${c===2}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1181,7 +1181,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           );
 
           ${h}
-        } else if (${u===3}) {
+        } else if (${c===3}) {
           ${d} values = ${d}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function sL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function _i(e,t,n,r){let a=sL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new Jb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new Jb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new aL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var oL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=iL(t);this.userCode=`
+    `}};function aL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function _i(e,t,n,r){let a=aL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],c,h;n==="mean"?c=i===0?new Qb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Qb({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):c=new rL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=s,s=r.runWebGLProgram(c,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var iL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=sL(t);this.userCode=`
     void main() {
       ${r} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function iL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var lL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ot(this.rank),a=Fb("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function sL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var oL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ot(this.rank),a=$b("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${r} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1215,14 +1215,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function pp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lL(e.shape,t):new oL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function uL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=pp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=sd(e.dtype),g=_i(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function aA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return uL(a,s,i,n)}var cL={kernelName:Gs,backendName:"webgl",kernelFunc:aA};function In(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=nA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=pp(a,s,i);return c}var hL={kernelName:Ys,backendName:"webgl",kernelFunc:In},Qb=1e3;function fp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),C=[N,T],F=Math.max(y,g),P=n?N.shape[1]:N.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",H=l!=null?dp(l,!0):null,X=W||V||U||H!=null,G;if((p===1||f===1)&&P>Qb&&X===!1){let Y=N,ae=T;n&&(Y=In({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),C.push(Y)),r&&(ae=In({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),C.push(ae));let te=f!==1,oe=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[F,P,1]}}),C.push(Q));let he=f===1?2:1,le=ae;oe&&(le=xe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,P]}}),C.push(le));let fe=Yb({inputs:{a:Q,b:le},backend:a});G=aA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),C.push(fe)}else{let Y=nr(e.dtype,t.dtype),ae=new qb(_,x,[F,p,f],n,r,W,H,V,U),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),U){let oe=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(oe),C.push(oe)}G=a.runWebGLProgram(ae,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});C.push(G);for(let Y of C)a.disposeIntermediateTensorInfo(Y);return ee}function dL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return fp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var pL={kernelName:Js,backendName:"webgl",kernelFunc:dL},e_="return abs(x);";function fL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Mb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,e_):a=new La(r.shape,e_),n.runWebGLProgram(a,[r],r.dtype)}var mL={kernelName:Yi,backendName:"webgl",kernelFunc:fL},AL=gr+`
+    `}};function pp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oL(e.shape,t):new iL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function lL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=pp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:c},attrs:{shape:[m,f]},backend:r}),y=sd(e.dtype),g=_i(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),w}function iA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return lL(a,s,i,n)}var uL={kernelName:Gs,backendName:"webgl",kernelFunc:iA};function In(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c<l.length;c++)l[c]=a.shape[s[c]];let u;if(i.shouldExecuteOnCPU([a])){let c=i.texData.get(a.dataId).values,h=aA(c,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(u.dataId);d.values=h}else u=pp(a,s,i);return u}var cL={kernelName:Ys,backendName:"webgl",kernelFunc:In},e_=1e3;function fp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),C=[N,T],F=Math.max(y,g),P=n?N.shape[1]:N.shape[2],W=s!=null,V=i!=null,U=l==="leakyrelu",j=l!=null?dp(l,!0):null,X=W||V||U||j!=null,G;if((p===1||f===1)&&P>e_&&X===!1){let Y=N,ae=T;n&&(Y=In({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),C.push(Y)),r&&(ae=In({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),C.push(ae));let te=f!==1,oe=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[F,P,1]}}),C.push(Q));let he=f===1?2:1,le=ae;oe&&(le=xe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,P]}}),C.push(le));let fe=Jb({inputs:{a:Q,b:le},backend:a});G=iA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),C.push(fe)}else{let Y=nr(e.dtype,t.dtype),ae=new Xb(_,x,[F,p,f],n,r,W,j,V,U),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),U){let oe=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(oe),C.push(oe)}G=a.runWebGLProgram(ae,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});C.push(G);for(let Y of C)a.disposeIntermediateTensorInfo(Y);return ee}function hL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return fp({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var dL={kernelName:Js,backendName:"webgl",kernelFunc:hL},t_="return abs(x);";function pL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Fb(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,t_):a=new La(r.shape,t_),n.runWebGLProgram(a,[r],r.dtype)}var fL={kernelName:Yi,backendName:"webgl",kernelFunc:pL},mL=gr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,yL=Xe({opSnippet:AL}),gL={kernelName:Ji,backendName:"webgl",kernelFunc:yL},xL=gr+`
+`,AL=Xe({opSnippet:mL}),yL={kernelName:Ji,backendName:"webgl",kernelFunc:AL},gL=gr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,wL=Xe({opSnippet:xL}),bL={kernelName:Qi,backendName:"webgl",kernelFunc:wL},t_="return a + b;",_L=en({opSnippet:t_,packedOpSnippet:t_,supportsComplex:!0,cpuKernelImpl:Gz}),vL={kernelName:va,backendName:"webgl",kernelFunc:_L},kL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,xL=Xe({opSnippet:gL}),wL={kernelName:Qi,backendName:"webgl",kernelFunc:xL},n_="return a + b;",bL=en({opSnippet:n_,packedOpSnippet:n_,supportsComplex:!0,cpuKernelImpl:Hz}),_L={kernelName:va,backendName:"webgl",kernelFunc:bL},vL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,wL=Xe({opSnippet:xL}),bL={kernelName:Qi,back
         float result = ${r};
         setOutput(result);
       }
-    `}},IL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},kL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,wL=Xe({opSnippet:xL}),bL={kernelName:Qi,back
         vec4 result = ${r};
         setOutput(result);
       }
-    `}};function mp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return On({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=mp({inputs:r.slice(0,o),backend:n}),c=mp({inputs:r.slice(o),backend:n});return mp({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>nr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new IL(r[0].shape,s):new kL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var NL={kernelName:is,backendName:"webgl",kernelFunc:mp};function SL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("all",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var TL={kernelName:vh,backendName:"webgl",kernelFunc:SL};function CL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("any",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var EL={kernelName:kh,backendName:"webgl",kernelFunc:CL},RL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function mp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return On({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=mp({inputs:r.slice(0,o),backend:n}),u=mp({inputs:r.slice(o),backend:n});return mp({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>nr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new kL(r[0].shape,s):new vL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var IL={kernelName:is,backendName:"webgl",kernelFunc:mp};function NL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var SL={kernelName:vh,backendName:"webgl",kernelFunc:NL};function TL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var CL={kernelName:kh,backendName:"webgl",kernelFunc:TL},EL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1258,23 +1258,23 @@ return log(x + sqrt(x * x - 1.0));`,wL=Xe({opSnippet:xL}),bL={kernelName:Qi,back
         }
         setOutput(float(bestIndex));
       }
-    `}},ML=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),c=dn("coords",o),u,h;if(s===1){h=o+1;let N=ot(h);u=`
-        ${N} sourceLocR = ${N}(${c.join()}, 0);
-        ++${c[o-1]};
-        ${N} sourceLocG = ${N}(${c.join()}, 0);
-        ++${c[o-2]};
-        ${N} sourceLocA = ${N}(${c.join()}, 0);
-        --${c[o-1]};
-        ${N} sourceLocB = ${N}(${c.join()}, 0);
-        --${c[o-2]};`}else h=o,u=`
+    `}},RL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),u=dn("coords",o),c,h;if(s===1){h=o+1;let N=ot(h);c=`
+        ${N} sourceLocR = ${N}(${u.join()}, 0);
+        ++${u[o-1]};
+        ${N} sourceLocG = ${N}(${u.join()}, 0);
+        ++${u[o-2]};
+        ${N} sourceLocA = ${N}(${u.join()}, 0);
+        --${u[o-1]};
+        ${N} sourceLocB = ${N}(${u.join()}, 0);
+        --${u[o-2]};`}else h=o,c=`
         ${l} sourceLocR = coords;
-        ++${c[o-1]};
+        ++${u[o-1]};
         ${l} sourceLocG = coords;
-        ++${c[o-2]};
+        ++${u[o-2]};
         ${l} sourceLocA = coords;
-        --${c[o-1]};
+        --${u[o-1]};
         ${l} sourceLocB = coords;
-        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=dn("sourceLocR",h-1).concat("inIdx.r"),A=dn("sourceLocG",h-1).concat("inIdx.g"),y=dn("sourceLocB",h-1).concat("inIdx.b"),g=dn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
+        --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=dn("sourceLocR",h-1).concat("inIdx.r"),A=dn("sourceLocG",h-1).concat("inIdx.g"),y=dn("sourceLocB",h-1).concat("inIdx.b"),g=dn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                              getBestIndicesAChannel(${A.join()}),
                              getBestIndicesAChannel(${y.join()}),
@@ -1294,9 +1294,9 @@ return log(x + sqrt(x * x - 1.0));`,wL=Xe({opSnippet:xL}),bL={kernelName:Qi,back
       ${x}
       void main() {
         ${l} coords = getOutputCoords();
-        bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
-        bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
-        ${u}
+        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
+        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
+        ${c}
         ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
           sourceLocB${p}, sourceLocA${p}) * ${t};
         ivec4 inIdx = srcIdx;
@@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,wL=Xe({opSnippet:xL}),bL={kernelName:Qi,back
         }
         setOutput(bestIndex);
       }
-    `}};function n_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new RL(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=n_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function r_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new ML(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=r_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function a_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=n_(e,c,r);s.push(u);let h=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return r_(e,t,r)}function FL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=a_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var $L={kernelName:os,backendName:"webgl",kernelFunc:FL};function DL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=a_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var OL={kernelName:du,backendName:"webgl",kernelFunc:DL},zL=gr+`
+    `}};function r_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new EL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=r_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function a_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new RL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=a_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function s_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=r_(e,u,r);s.push(c);let h=xe({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return a_(e,t,r)}function ML(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=s_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var FL={kernelName:os,backendName:"webgl",kernelFunc:ML};function $L(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=s_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var DL={kernelName:du,backendName:"webgl",kernelFunc:$L},OL=gr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,PL=Xe({opSnippet:zL}),LL={kernelName:eo,backendName:"webgl",kernelFunc:PL},WL=gr+"return log(x + sqrt(x * x + 1.0));",BL=Xe({opSnippet:WL}),VL={kernelName:to,backendName:"webgl",kernelFunc:BL},UL=gr+`
+`,zL=Xe({opSnippet:OL}),PL={kernelName:eo,backendName:"webgl",kernelFunc:zL},LL=gr+"return log(x + sqrt(x * x + 1.0));",WL=Xe({opSnippet:LL}),BL={kernelName:to,backendName:"webgl",kernelFunc:WL},VL=gr+`
   return atan(x);
-`,jL=Xe({opSnippet:UL}),HL={kernelName:no,backendName:"webgl",kernelFunc:jL},GL=QP+`
+`,UL=Xe({opSnippet:VL}),jL={kernelName:no,backendName:"webgl",kernelFunc:UL},HL=JP+`
   return atan(a, b);
-`,qL=`
+`,GL=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+eL+`
+  `+QP+`
   return result;
-`,XL=en({opSnippet:GL,packedOpSnippet:qL}),KL={kernelName:ao,backendName:"webgl",kernelFunc:XL},ZL=gr+`
+`,qL=en({opSnippet:HL,packedOpSnippet:GL}),XL={kernelName:ao,backendName:"webgl",kernelFunc:qL},KL=gr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelName:ro,backendName:"webgl",kernelFunc:YL},fc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZL=Xe({opSnippet:KL}),YL={kernelName:ro,backendName:"webgl",kernelFunc:ZL},fc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${p});
 
@@ -1356,7 +1356,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           int minMaxPosition = 0;
           float avgValue = 0.0;
 
-          for (int wR = 0; wR < ${u};
+          for (int wR = 0; wR < ${c};
               wR += ${l}) {
             int xR = xRCorner + wR;
 
@@ -1365,7 +1365,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
             }
 
             for (int wC = 0; wC < ${h};
-                wC += ${c}) {
+                wC += ${u}) {
               int xC = xCCorner + wC;
 
               if (xC < 0 || xC >= ${e.inWidth}) {
@@ -1424,7 +1424,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wR = 0; wR < ${u};
+        for (int wR = 0; wR < ${c};
             wR += ${l}) {
           int xR = xRCorner + wR;
 
@@ -1433,13 +1433,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           }
 
           for (int wC = 0; wC < ${b}; wC += 4) {
-            int xC = xCCorner + wC * ${c};
+            int xC = xCCorner + wC * ${u};
 
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${c}, d),
-              getValue(batch, xR, xC + 2 * ${c}, d),
-              getValue(batch, xR, xC + 3 * ${c}, d)
+              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + 2 * ${u}, d),
+              getValue(batch, xR, xC + 3 * ${u}, d)
             );
 
             ${x}
@@ -1458,7 +1458,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           } else if (${_===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${c}, d),
+              getValue(batch, xR, xC + ${u}, d),
               initializationValue,
               initializationValue
             );
@@ -1467,8 +1467,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           } else if (${_===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${c}, d),
-              getValue(batch, xR, xC + 2 * ${c}, d),
+              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + 2 * ${u}, d),
               initializationValue
             );
 
@@ -1477,7 +1477,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(${w});
       }
-    `}},sA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
+    `}},oA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${m}, ${A}, ${y});
@@ -1499,7 +1499,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           int minMaxPosition = 0;
 
           for (int wD = 0; wD < ${d};
-              wD += ${c}) {
+              wD += ${u}) {
             int xD = xDCorner + wD;
 
             if (xD < 0 || xD >= ${e.inDepth}) {
@@ -1507,7 +1507,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
             }
 
             for (int wR = 0; wR < ${p};
-                wR += ${u}) {
+                wR += ${c}) {
               int xR = xRCorner + wR;
 
               if (xR < 0 || xR >= ${e.inHeight}) {
@@ -1579,7 +1579,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         count = 0.0;
 
         for (int wD = 0; wD < ${d};
-            wD += ${c}) {
+            wD += ${u}) {
           int xD = xDCorner + wD;
 
           if (xD < 0 || xD >= ${e.inDepth}) {
@@ -1587,7 +1587,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           }
 
           for (int wR = 0; wR < ${p};
-            wR += ${u}) {
+            wR += ${c}) {
             int xR = xRCorner + wR;
 
             if (xR < 0 || xR >= ${e.inHeight}) {
@@ -1640,8 +1640,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           setOutput(${_});
         }
       }
-    `}};function QL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return On({inputs:{x:a},backend:n});let h=new fc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var eW={kernelName:ls,backendName:"webgl",kernelFunc:QL};function tW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new sA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var nW={kernelName:pu,backendName:"webgl",kernelFunc:tW},rW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
-      const ivec2 pads = ivec2(${c}, ${u});
+    `}};function JL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return On({inputs:{x:a},backend:n});let h=new fc(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var QL={kernelName:ls,backendName:"webgl",kernelFunc:JL};function eW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new oA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var tW={kernelName:pu,backendName:"webgl",kernelFunc:eW},nW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
+      const ivec2 pads = ivec2(${u}, ${c});
       const float avgMultiplier = float(${h});
 
       void main() {
@@ -1682,7 +1682,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},aW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
+    `}},rW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
       const ivec3 pads = ivec3(${p}, ${f}, ${m});
       const float avgMultiplier = float(${A});
 
@@ -1701,7 +1701,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${u};
+        for (int wD = 0; wD < ${c};
             wD += ${o}) {
           float dyD = float(dyDCorner + wD) / ${a}.0;
 
@@ -1721,7 +1721,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
             int idyR = int(dyR);
 
             for (int wC = 0; wC < ${d};
-                wC += ${c}) {
+                wC += ${u}) {
               float dyC = float(dyCCorner + wC) / ${i}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function sW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new aW(d);return n.runWebGLProgram(p,[a],i.dtype)}var iW={kernelName:Nh,backendName:"webgl",kernelFunc:sW};function oW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;kl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=new rW(u);return n.runWebGLProgram(h,[a],i.dtype)}var lW={kernelName:Ih,backendName:"webgl",kernelFunc:oW};function uW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return fp({a,b:s,transposeA:i,transposeB:o,backend:n})}var cW={kernelName:us,backendName:"webgl",kernelFunc:uW},hW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function aW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new rW(d);return n.runWebGLProgram(p,[a],i.dtype)}var sW={kernelName:Nh,backendName:"webgl",kernelFunc:aW};function iW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;kl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=new nW(c);return n.runWebGLProgram(h,[a],i.dtype)}var oW={kernelName:Ih,backendName:"webgl",kernelFunc:iW};function lW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return fp({a,b:s,transposeA:i,transposeB:o,backend:n})}var uW={kernelName:us,backendName:"webgl",kernelFunc:lW},cW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},dW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},hW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},pW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new dW(r.shape,a.shape,s.shape,u,h,l):new hW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},fW={kernelName:bs,backendName:"webgl",kernelFunc:pW},AW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=mW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${iA[o]} = start[${o}] + coords.${iA[o]};`);a=`
+    `}},dW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new hW(r.shape,a.shape,s.shape,c,h,l):new cW(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},pW={kernelName:bs,backendName:"webgl",kernelFunc:dW},mW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=fW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${lA[o]} = start[${o}] + coords.${lA[o]};`);a=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${a}
         setOutput(getSource(${r}));
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},iA=["x","y","z","w","u","v"];function mW(e){if(e===1)return"sourceLoc";if(e<=6)return iA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var yW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=dn("coords",this.rank),r=dn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},lA=["x","y","z","w","u","v"];function fW(e){if(e===1)return"sourceLoc";if(e<=6)return lA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var AW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=dn("coords",this.rank),r=dn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${r[this.rank-1]};
@@ -1790,7 +1790,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
       }
     `,l=this.rank<=4?`sourceLoc = coords +
-            ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
+            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
 `);this.userCode=`
       uniform int start[${this.rank}];
       void main() {
@@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${o}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function gW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=ln.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function mc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=ln.parseSliceParams(a,s,i);if(ln.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=dP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=ln.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yW(l):new AW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),gW(a,o,l,n)}var xW={kernelName:Wo,backendName:"webgl",kernelFunc:mc},wW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=In({inputs:{x:f},backend:n,attrs:{perm:c}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=mc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},bW={kernelName:fu,backendName:"webgl",kernelFunc:wW};function _W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=Rb(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var vW={kernelName:Sh,backendName:"webgl",kernelFunc:_W},kW="return float(a != b);",s_=en({opSnippet:kW,dtype:"bool"}),IW={kernelName:To,backendName:"webgl",kernelFunc:s_};function Ac(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.real},backend:n})}var NW={kernelName:Xh,backendName:"webgl",kernelFunc:Ac},SW="return float(int(x));";function TW(e,t){let n=new La(e.shape,SW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function oA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return On({inputs:{x:a},backend:n});let i=Ct(a.shape),o=oA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Wa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Ac({inputs:{input:a},backend:n}),o=oA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=On({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return TW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=s_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var CW={kernelName:cs,backendName:"webgl",kernelFunc:oA},i_="return ceil(x);",EW=Xe({opSnippet:i_,packedOpSnippet:i_,cpuKernelImpl:Xz}),RW={kernelName:hs,backendName:"webgl",kernelFunc:EW},MW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function yW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=ln.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function mc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=ln.parseSliceParams(a,s,i);if(ln.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=hP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=ln.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AW(l):new mW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),yW(a,o,l,n)}var gW={kernelName:Wo,backendName:"webgl",kernelFunc:mc},xW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=In({inputs:{x:f},backend:n,attrs:{perm:u}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:c}}),y=mc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},wW={kernelName:fu,backendName:"webgl",kernelFunc:xW};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=Mb(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var _W={kernelName:Sh,backendName:"webgl",kernelFunc:bW},vW="return float(a != b);",i_=en({opSnippet:vW,dtype:"bool"}),kW={kernelName:To,backendName:"webgl",kernelFunc:i_};function Ac(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.real},backend:n})}var IW={kernelName:Xh,backendName:"webgl",kernelFunc:Ac},NW="return float(int(x));";function SW(e,t){let n=new La(e.shape,NW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function uA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return On({inputs:{x:a},backend:n});let i=Ct(a.shape),o=uA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Wa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Ac({inputs:{input:a},backend:n}),o=uA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=On({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return SW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=i_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var TW={kernelName:cs,backendName:"webgl",kernelFunc:uA},o_="return ceil(x);",CW=Xe({opSnippet:o_,packedOpSnippet:o_,cpuKernelImpl:qz}),EW={kernelName:hs,backendName:"webgl",kernelFunc:CW},RW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},FW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},MW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function $W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new FW(a.shape):o=new MW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var DW={kernelName:ka,backendName:"webgl",kernelFunc:$W},OW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function FW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new MW(a.shape):o=new RW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var $W={kernelName:ka,backendName:"webgl",kernelFunc:FW},DW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function o_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new OW(r.shape),i=[o_(r,a.complexTensorInfos.real),o_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var PW={kernelName:mu,backendName:"webgl",kernelFunc:zW},LW=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
+    `}};function l_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function OW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new DW(r.shape),i=[l_(r,a.complexTensorInfos.real),l_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var zW={kernelName:mu,backendName:"webgl",kernelFunc:OW},PW=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1851,18 +1851,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${n.join(`
         `)}
       }
-    `}},WW=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ot(r),s=dn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},LW=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ot(r),s=dn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),c=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
-            getT0(${u}), vec2(${c.join()}));
+            getT0(${c}), vec2(${u.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
             getT${f}(${Ap(i,l,m)}),
-            vec2(${Ap(c,l,m)}));
+            vec2(${Ap(u,l,m)}));
         }`}let d=o.length,p=o[o.length-1];h+=`
         return getChannel(
           getT${d}(${Ap(i,l,p)}),
-          vec2(${Ap(c,l,p)}));`,this.userCode=`
+          vec2(${Ap(u,l,p)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -1888,7 +1888,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(result);
       }
-    `}};function Ap(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function yp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.imag},backend:n})}var BW={kernelName:Bh,backendName:"webgl",kernelFunc:yp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>Ac({inputs:{input:f},backend:n})),u=e.map(f=>yp({inputs:{input:f},backend:n})),h=Fl(c,t,n),d=Fl(u,t,n),p=Wa({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=l_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=Kz(h,u,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Fl(e.slice(0,c),t,n),h=Fl(e.slice(c),t,n),d=Fl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new WW(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=l_(e,t,n),i=new LW(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function l_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function u_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(c=>c.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>v.sizeFromShape(c.shape)>0);if(o.length===1)return On({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return R.assertParamsConsistent(l,s),Fl(o,s,n)}var VW={kernelName:so,backendName:"webgl",kernelFunc:u_},c_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
+    `}};function Ap(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function yp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return On({inputs:{x:a.complexTensorInfos.imag},backend:n})}var WW={kernelName:Bh,backendName:"webgl",kernelFunc:yp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>Ac({inputs:{input:f},backend:n})),c=e.map(f=>yp({inputs:{input:f},backend:n})),h=Fl(u,t,n),d=Fl(c,t,n),p=Wa({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=u_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=Xz(h,c,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=Fl(e.slice(0,u),t,n),h=Fl(e.slice(u),t,n),d=Fl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new LW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=u_(e,t,n),i=new PW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function u_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function c_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return On({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),Fl(o,s,n)}var BW={kernelName:so,backendName:"webgl",kernelFunc:c_},h_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:a?w=`float activation(float a) {
@@ -1918,14 +1918,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
         for (int wR = 0; wR < ${h}; wR++) {
-          int xR = xRCorner + wR * ${c};
+          int xR = xRCorner + wR * ${u};
 
           if (xR < 0 || xR >= ${e.inHeight}) {
             continue;
           }
 
           for (int wC = 0; wC < ${d}; wC++) {
-            int xC = xCCorner + wC * ${u};
+            int xC = xCCorner + wC * ${c};
 
             if (xC < 0 || xC >= ${e.inWidth}) {
               continue;
@@ -2022,7 +2022,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${b}
         setOutput(result);
       }
-    `}},UW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},VW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${r});
 
@@ -2040,7 +2040,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         // y(yF, yR, yC, d2). ? = to be determined. : = across all
         // values in that axis.
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${u}; wF++) {
+        for (int wF = 0; wF < ${c}; wF++) {
           int xF = xFCorner + wF * ${o};
 
           if (xF < 0 || xF >= ${e.inDepth}) {
@@ -2055,7 +2055,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
             }
 
             for (int wC = 0; wC < ${d}; wC++) {
-              int xC = xCCorner + wC * ${c};
+              int xC = xCCorner + wC * ${u};
 
               if (xC < 0 || xC >= ${e.inWidth}) {
                 continue;
@@ -2110,18 +2110,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},jW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=hn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
+    `}},UW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=hn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
           blockIndex = rc.y + ${_};
           pos = rc.x + ${b};
 
           if(blockIndex < ${e[1]} && pos < ${e[0]}) {
             offsetY = int(blockIndex / (${l})) * ${i} - ${p};
-            d0 = offsetY + ${u} * (pos / ${f});
+            d0 = offsetY + ${c} * (pos / ${f});
 
             if(d0 < ${t[y]} && d0 >= 0) {
 
               offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
-              d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
+              d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.));
 
               if(d1 < ${t[g]} && d1 >= 0) {
 
@@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
         ${m.output} = result;
       }
-    `}};function h_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>Qb,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=fp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(lc(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=fp({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,C.shape=n.outShape,A=On({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function d_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new jW(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),C=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(C);let F=a!=null,P=s!=null,W=o==="leakyrelu",V=o?dp(o,!0):null,U=new qb(C.shape,x.shape,[1,A,n.outChannels],g,w,F,V,P,W),H=[C,x];if(a&&H.push(a),P&&H.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=h_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=d_({x:a,filter:s,convInfo:d,backend:n});else{let m=new c_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var GW={kernelName:ds,backendName:"webgl",kernelFunc:HW},qW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function d_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>e_,w=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=fp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(lc(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=fp({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,C.shape=n.outShape,A=On({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function p_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new UW(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),C=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(C);let F=a!=null,P=s!=null,W=o==="leakyrelu",V=o?dp(o,!0):null,U=new Xb(C.shape,x.shape,[1,A,n.outChannels],g,w,F,V,P,W),j=[C,x];if(a&&j.push(a),P&&j.push(s),W){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,j,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function jW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=d_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=p_({x:a,filter:s,convInfo:d,backend:n});else{let m=new h_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var HW={kernelName:ds,backendName:"webgl",kernelFunc:jW},GW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2196,15 +2196,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},XW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
+    `}},qW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d1 = coords[${u}];
+        int d1 = coords[${c}];
 
-        ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
+        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
         int dyRCorner = dyCorner.x;
         int dyCCorner = dyCorner.y;
 
@@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},KW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},XW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2291,8 +2291,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},ZW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${o}, ${l}, ${c});
+    `}},KW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${o}, ${l}, ${u});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function YW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new qW(d);return n.runWebGLProgram(p,[a,s],"float32")}var JW={kernelName:Ch,backendName:"webgl",kernelFunc:YW};function QW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(c),d=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new XW(d);return n.runWebGLProgram(p,[a,s],"float32")}var eB={kernelName:ps,backendName:"webgl",kernelFunc:QW};function tB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new UW(c);return n.runWebGLProgram(u,[a,s],"float32")}var nB={kernelName:Au,backendName:"webgl",kernelFunc:tB};function rB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=R.computeConv3DInfo(a.shape,l,i,1,o),u=new KW(c);return n.runWebGLProgram(u,[a,s],"float32")}var aB={kernelName:Eh,backendName:"webgl",kernelFunc:rB};function sB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=R.computeConv3DInfo(l,s.shape,o,1,i),u=new ZW(c);return n.runWebGLProgram(u,[a,s],"float32")}var iB={kernelName:Rh,backendName:"webgl",kernelFunc:sB},oB=Gb+`
+    `}};function ZW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new GW(d);return n.runWebGLProgram(p,[a,s],"float32")}var YW={kernelName:Ch,backendName:"webgl",kernelFunc:ZW};function JW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(u),d=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new qW(d);return n.runWebGLProgram(p,[a,s],"float32")}var QW={kernelName:ps,backendName:"webgl",kernelFunc:JW};function eB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new VW(u);return n.runWebGLProgram(c,[a,s],"float32")}var tB={kernelName:Au,backendName:"webgl",kernelFunc:eB};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=R.computeConv3DInfo(a.shape,l,i,1,o),c=new XW(u);return n.runWebGLProgram(c,[a,s],"float32")}var rB={kernelName:Eh,backendName:"webgl",kernelFunc:nB};function aB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=R.computeConv3DInfo(l,s.shape,o,1,i),c=new KW(u);return n.runWebGLProgram(c,[a,s],"float32")}var sB={kernelName:Rh,backendName:"webgl",kernelFunc:aB},iB=qb+`
   return cos(x);
-`,lB=Xe({opSnippet:oB}),uB={kernelName:fs,backendName:"webgl",kernelFunc:lB},cB=`
+`,oB=Xe({opSnippet:iB}),lB={kernelName:fs,backendName:"webgl",kernelFunc:oB},uB=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,hB=Xe({opSnippet:cB}),dB={kernelName:io,backendName:"webgl",kernelFunc:hB},pB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,cB=Xe({opSnippet:uB}),hB={kernelName:io,backendName:"webgl",kernelFunc:cB},dB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${g});
       void main() {
@@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           setOutput(newValue);
         }
       }
-    `}},fB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new pB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},mB={kernelName:oo,backendName:"webgl",kernelFunc:fB},m_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${p_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},pB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new dB(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},fB={kernelName:oo,backendName:"webgl",kernelFunc:pB},A_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${f_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
         ${ot(r)} coords = getOutputCoords();
-        int end = ${f_(r,"coords")};
+        int end = ${m_(r,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${f_(r,"coords")} = idx;
-          val += getX(${p_(r,"coords")});
+          ${m_(r,"coords")} = idx;
+          val += getX(${f_(r,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function p_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function f_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function AB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=R.getAxesPermutation([s],l),u=a;c!=null&&(u=In({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=On({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new m_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new m_(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=R.getUndoAxesPermutation(c),m=In({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var yB={kernelName:ms,backendName:"webgl",kernelFunc:AB};function gB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=Rb(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=qz(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var xB={kernelName:Mh,backendName:"webgl",kernelFunc:gB},wB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function f_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function m_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function mB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=R.getAxesPermutation([s],l),c=a;u!=null&&(c=In({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=On({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new A_(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new A_(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=In({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var AB={kernelName:ms,backendName:"webgl",kernelFunc:mB};function yB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Mb(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=Gz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var gB={kernelName:Mh,backendName:"webgl",kernelFunc:yB},xB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2447,7 +2447,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new wB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var _B={kernelName:lo,backendName:"webgl",kernelFunc:bB},A_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function wB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new xB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var bB={kernelName:lo,backendName:"webgl",kernelFunc:wB},y_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:a?A=`float activation(float a) {
@@ -2460,7 +2460,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${A}
 
-      const ivec2 strides = ivec2(${c}, ${u});
+      const ivec2 strides = ivec2(${u}, ${c});
       const ivec2 pads = ivec2(${o}, ${l});
 
       void main() {
@@ -2503,13 +2503,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${y}
         setOutput(result);
       }
-    `}},y_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
+    `}},g_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
           vec4 xTexelR${b}C${_*2} = vec4(0.);
           vec4 wR${b}C${_} = vec4(0.);
           vec4 xR${b}C${_} = vec4(0.);`;for(let b=0;b<p;b++)for(let _=0;_<m;_++){let x=_*2;if(A+=`
           xR = xRCorner + ${b*h};
           xC = xCCorner + ${x*d};
-        `,u===1){if(x<f&&(l%2==1?A+=`
+        `,c===1){if(x<f&&(l%2==1?A+=`
                 xCOffset = xC + 1;
                 if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
@@ -2575,7 +2575,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
                 `}}else x<f&&(A+=`
               if(xR >= 0 && xR < ${s}) {
             `,l%2==1?(A+=`
-                xCOffset = xC + 1 - ${u};
+                xCOffset = xC + 1 - ${c};
                 if(xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
                 } else {
@@ -2592,7 +2592,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
                   xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
               `,x+1<f&&(A+=`
                   vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${u};
+                  xCOffset = xC + 1 + ${c};
                   if(xCOffset >= 0 && xCOffset < ${i}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
@@ -2604,7 +2604,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
                   xTexelR${b}C${x} = vec4(0.);
                 }
 
-                xCOffset = xC + ${u};
+                xCOffset = xC + ${c};
                 if(xCOffset >= 0 && xCOffset < ${i}) {
                   xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
                 } else {
@@ -2633,7 +2633,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }`,g="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${y}
 
-      const ivec2 strides = ivec2(${c}, ${u});
+      const ivec2 strides = ivec2(${u}, ${c});
       const ivec2 pads = ivec2(${o}, ${l});
 
       void main() {
@@ -2656,7 +2656,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${g}
         setOutput(result);
       }
-    `}};function vB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new y_(h):d=new A_(h),n.runWebGLProgram(d,[a,s],"float32")}var kB={kernelName:As,backendName:"webgl",kernelFunc:vB},IB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new g_(h):d=new y_(h),n.runWebGLProgram(d,[a,s],"float32")}var vB={kernelName:As,backendName:"webgl",kernelFunc:_B},kB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2691,7 +2691,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},NB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},IB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2736,15 +2736,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function SB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new IB(h);return n.runWebGLProgram(d,[a,s],"float32")}var TB={kernelName:Fh,backendName:"webgl",kernelFunc:SB};function CB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new NB(h);return n.runWebGLProgram(d,[a,s],"float32")}var EB={kernelName:$h,backendName:"webgl",kernelFunc:CB},RB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function NB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new kB(h);return n.runWebGLProgram(d,[a,s],"float32")}var SB={kernelName:Fh,backendName:"webgl",kernelFunc:NB};function TB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new IB(h);return n.runWebGLProgram(d,[a,s],"float32")}var CB={kernelName:$h,backendName:"webgl",kernelFunc:TB},EB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function MB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new RB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var FB={kernelName:Dh,backendName:"webgl",kernelFunc:MB},$B=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
+    `}};function RB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new EB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var MB={kernelName:Dh,backendName:"webgl",kernelFunc:RB},FB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
-      const ivec2 pads = ivec2(${u}, ${h});
+      const ivec2 pads = ivec2(${c}, ${h});
       const float neg_infinity = -3.4e38;
 
       void main() {
@@ -2762,7 +2762,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
           if (hIn >= 0 && hIn < ${t}) {
             for (int w = 0; w < ${o}; w++) {
-              int wIn = wBeg + w * ${c};
+              int wIn = wBeg + w * ${u};
 
               if (wIn >= 0 && wIn < ${n}) {
                 float xVal = getX(batch, hIn, wIn, d1);
@@ -2780,7 +2780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function DB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new $B(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var OB={kernelName:yu,backendName:"webgl",kernelFunc:DB},zB="return (x >= 0.0) ? x : (exp(x) - 1.0);",PB=`
+    `}};function $B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new FB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var DB={kernelName:yu,backendName:"webgl",kernelFunc:$B},OB="return (x >= 0.0) ? x : (exp(x) - 1.0);",zB=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2789,12 +2789,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,LB=Xe({opSnippet:zB,packedOpSnippet:PB}),WB={kernelName:uo,backendName:"webgl",kernelFunc:LB},BB="return (b >= 1.0) ? a : a * (b + 1.0);",VB=`
+`,PB=Xe({opSnippet:OB,packedOpSnippet:zB}),LB={kernelName:uo,backendName:"webgl",kernelFunc:PB},WB="return (b >= 1.0) ? a : a * (b + 1.0);",BB=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,UB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(VB,r.shape,a.shape):new Ml(BB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},jB={kernelName:Ph,backendName:"webgl",kernelFunc:UB},HB=`
+`,VB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pc(BB,r.shape,a.shape):new Ml(WB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},UB={kernelName:Ph,backendName:"webgl",kernelFunc:VB},jB=`
   return vec4(equal(a, b));
-`,GB="return float(a == b);",qB=en({opSnippet:GB,packedOpSnippet:HB,dtype:"bool"}),XB={kernelName:ho,backendName:"webgl",kernelFunc:qB},KB=`
+`,HB="return float(a == b);",GB=en({opSnippet:HB,packedOpSnippet:jB,dtype:"bool"}),qB={kernelName:ho,backendName:"webgl",kernelFunc:GB},XB=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -2809,7 +2809,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,ZB=Xe({opSnippet:KB}),YB={kernelName:co,backendName:"webgl",kernelFunc:ZB},g_="return exp(x);",x_=Xe({opSnippet:g_,packedOpSnippet:g_,cpuKernelImpl:Zz}),JB={kernelName:gs,backendName:"webgl",kernelFunc:x_};function lA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var QB={kernelName:po,backendName:"webgl",kernelFunc:lA},w_="return exp(x) - 1.0;",eV=Xe({opSnippet:w_,packedOpSnippet:w_,cpuKernelImpl:Yz}),tV={kernelName:fo,backendName:"webgl",kernelFunc:eV},b_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,KB=Xe({opSnippet:XB}),ZB={kernelName:co,backendName:"webgl",kernelFunc:KB},x_="return exp(x);",w_=Xe({opSnippet:x_,packedOpSnippet:x_,cpuKernelImpl:Kz}),YB={kernelName:gs,backendName:"webgl",kernelFunc:w_};function cA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var JB={kernelName:po,backendName:"webgl",kernelFunc:cA},b_="return exp(x) - 1.0;",QB=Xe({opSnippet:b_,packedOpSnippet:b_,cpuKernelImpl:Zz}),eV={kernelName:fo,backendName:"webgl",kernelFunc:QB},__=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2842,13 +2842,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function __(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new b_("real",l,t),u=new b_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function nV(e){let{inputs:t,backend:n}=e,{input:r}=t;return __(r,!1,n)}var rV={kernelName:Lh,backendName:"webgl",kernelFunc:nV},aV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function v_(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new __("real",l,t),c=new __("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Wa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function tV(e){let{inputs:t,backend:n}=e,{input:r}=t;return v_(r,!1,n)}var nV={kernelName:Lh,backendName:"webgl",kernelFunc:tV},rV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function uA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new aV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var sV={kernelName:gu,backendName:"webgl",kernelFunc:uA},iV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function hA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new rV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var aV={kernelName:gu,backendName:"webgl",kernelFunc:hA},sV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -2862,7 +2862,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},oV={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new iV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},v_="return floor(x);",lV=Xe({opSnippet:v_,packedOpSnippet:v_,cpuKernelImpl:Jz}),uV={kernelName:xs,backendName:"webgl",kernelFunc:lV},cV=`
+    `}},iV={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new sV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},k_="return floor(x);",oV=Xe({opSnippet:k_,packedOpSnippet:k_,cpuKernelImpl:Yz}),lV={kernelName:xs,backendName:"webgl",kernelFunc:oV},uV=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2872,7 +2872,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
   } else {
     return NAN;
   }
-`,hV=`
+`,cV=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2893,7 +2893,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,dV=en({opSnippet:cV,packedOpSnippet:hV,dtype:"int32"}),pV={kernelName:ws,backendName:"webgl",kernelFunc:dV},fV=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
+`,hV=en({opSnippet:uV,packedOpSnippet:cV,dtype:"int32"}),dV={kernelName:ws,backendName:"webgl",kernelFunc:hV},pV=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2915,7 +2915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},mV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
+    `}},fV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
 
         ${t.output} = result;
       }
-    `}},yV={kernelName:ed,backendName:"webgl",kernelFunc:AV},$l;function AV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&($l==null&&($l=document.createElement("canvas").getContext("2d")),$l.canvas.width=l,$l.canvas.height=c,$l.drawImage(a,0,0,l,c),a=$l.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=Xn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new mV(h):new fV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function gV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(u),A=R.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=h_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=d_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?dp(p,!1):null,T=new c_(A,b,N,_,x),C=[a,s];if(i&&C.push(i),o&&C.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(T,C,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var xV={kernelName:Qs,backendName:"webgl",kernelFunc:gV};function wV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?dp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let C=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(C),f.push(C)}let N;y?N=new y_(A,b,g,_,x):N=new A_(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(C=>n.disposeIntermediateTensorInfo(C)),T}var bV={kernelName:ei,backendName:"webgl",kernelFunc:wV},_V=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ot(t.length),a=ot(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},AV={kernelName:ed,backendName:"webgl",kernelFunc:mV},$l;function mV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[u,l],h=[u,l,s];(o||i)&&($l==null&&($l=document.createElement("canvas").getContext("2d")),$l.canvas.width=l,$l.canvas.height=u,$l.drawImage(a,0,0,l,u),a=$l.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Xn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new fV(h):new pV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function yV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(c),A=R.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=d_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=p_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?dp(p,!1):null,T=new h_(A,b,N,_,x),C=[a,s];if(i&&C.push(i),o&&C.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(T,C,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var gV={kernelName:Qs,backendName:"webgl",kernelFunc:yV};function xV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?dp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let C=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(C),f.push(C)}let N;y?N=new g_(A,b,g,_,x):N=new y_(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(C=>n.disposeIntermediateTensorInfo(C)),T}var wV={kernelName:ei,backendName:"webgl",kernelFunc:xV},bV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ot(t.length),a=ot(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${r} strides = ${r}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -2960,21 +2960,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function vV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/c,c]}}),p=new _V(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var kV={kernelName:yo,backendName:"webgl",kernelFunc:vV},NV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),r=IV(e,2);this.userCode=`
+      `}};function _V(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new bV(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var vV={kernelName:yo,backendName:"webgl",kernelFunc:_V},IV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),r=kV(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function IV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function SV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=Qz(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new NV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var TV={kernelName:Ao,backendName:"webgl",kernelFunc:SV},CV="return float(a > b);",EV=`
+    `}};function kV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function NV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});h.push(d),h.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=Jz(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new IV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var SV={kernelName:Ao,backendName:"webgl",kernelFunc:NV},TV="return float(a > b);",CV=`
   return vec4(greaterThan(a, b));
-`,RV=en({opSnippet:CV,packedOpSnippet:EV,cpuKernelImpl:eP,dtype:"bool"}),MV={kernelName:go,backendName:"webgl",kernelFunc:RV},FV="return float(a >= b);",$V=`
+`,EV=en({opSnippet:TV,packedOpSnippet:CV,cpuKernelImpl:Qz,dtype:"bool"}),RV={kernelName:go,backendName:"webgl",kernelFunc:EV},MV="return float(a >= b);",FV=`
   return vec4(greaterThanEqual(a, b));
-`,DV=en({opSnippet:FV,packedOpSnippet:$V,dtype:"bool"}),OV={kernelName:_s,backendName:"webgl",kernelFunc:DV};function zV(e){let{inputs:t,backend:n}=e,{input:r}=t;return __(r,!0,n)}var PV={kernelName:Wh,backendName:"webgl",kernelFunc:zV},LV="return float(!isnan(x) && !isinf(x));",WV=Xe({opSnippet:LV,dtype:"bool"}),BV={kernelName:xo,backendName:"webgl",kernelFunc:WV},VV="return float(isinf(x));",UV=Xe({opSnippet:VV,dtype:"bool"}),jV={kernelName:wo,backendName:"webgl",kernelFunc:UV},HV="return float(isnan(x));",GV=Xe({opSnippet:HV,dtype:"bool"}),qV={kernelName:bo,backendName:"webgl",kernelFunc:GV},XV="return float(a < b);",KV=`
+`,$V=en({opSnippet:MV,packedOpSnippet:FV,dtype:"bool"}),DV={kernelName:_s,backendName:"webgl",kernelFunc:$V};function OV(e){let{inputs:t,backend:n}=e,{input:r}=t;return v_(r,!0,n)}var zV={kernelName:Wh,backendName:"webgl",kernelFunc:OV},PV="return float(!isnan(x) && !isinf(x));",LV=Xe({opSnippet:PV,dtype:"bool"}),WV={kernelName:xo,backendName:"webgl",kernelFunc:LV},BV="return float(isinf(x));",VV=Xe({opSnippet:BV,dtype:"bool"}),UV={kernelName:wo,backendName:"webgl",kernelFunc:VV},jV="return float(isnan(x));",HV=Xe({opSnippet:jV,dtype:"bool"}),GV={kernelName:bo,backendName:"webgl",kernelFunc:HV},qV="return float(a < b);",XV=`
   return vec4(lessThan(a, b));
-`,ZV=en({opSnippet:XV,packedOpSnippet:KV,cpuKernelImpl:tP,dtype:"bool"}),YV={kernelName:_o,backendName:"webgl",kernelFunc:ZV},JV="return float(a <= b);",QV=`
+`,KV=en({opSnippet:qV,packedOpSnippet:XV,cpuKernelImpl:eP,dtype:"bool"}),ZV={kernelName:_o,backendName:"webgl",kernelFunc:KV},YV="return float(a <= b);",JV=`
   return vec4(lessThanEqual(a, b));
-`,eU=en({opSnippet:JV,packedOpSnippet:QV,dtype:"bool"}),tU={kernelName:vo,backendName:"webgl",kernelFunc:eU};function nU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=nP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var rU={kernelName:Vh,backendName:"webgl",kernelFunc:nU},aU=`if (x < 0.0) return NAN;
-  return log(x);`,sU=`
+`,QV=en({opSnippet:YV,packedOpSnippet:JV,dtype:"bool"}),eU={kernelName:vo,backendName:"webgl",kernelFunc:QV};function tU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=tP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var nU={kernelName:Vh,backendName:"webgl",kernelFunc:tU},rU=`if (x < 0.0) return NAN;
+  return log(x);`,aU=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2983,16 +2983,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,iU=Xe({opSnippet:aU,packedOpSnippet:sU,cpuKernelImpl:rP}),oU={kernelName:Is,backendName:"webgl",kernelFunc:iU},lU="return log(1.0 + x);",uU=Xe({opSnippet:lU}),cU={kernelName:ko,backendName:"webgl",kernelFunc:uU},hU="return float(a >= 1.0 && b >= 1.0);",dU=`
+`,sU=Xe({opSnippet:rU,packedOpSnippet:aU,cpuKernelImpl:nP}),iU={kernelName:Is,backendName:"webgl",kernelFunc:sU},oU="return log(1.0 + x);",lU=Xe({opSnippet:oU}),uU={kernelName:ko,backendName:"webgl",kernelFunc:lU},cU="return float(a >= 1.0 && b >= 1.0);",hU=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,pU=en({opSnippet:hU,packedOpSnippet:dU,dtype:"bool"}),fU={kernelName:Io,backendName:"webgl",kernelFunc:pU},mU="return float(!(x >= 1.0));",AU=Xe({opSnippet:mU}),yU={kernelName:xu,backendName:"webgl",kernelFunc:AU},gU="return float(a >= 1.0 || b >= 1.0);",xU=`
+`,dU=en({opSnippet:cU,packedOpSnippet:hU,dtype:"bool"}),pU={kernelName:Io,backendName:"webgl",kernelFunc:dU},fU="return float(!(x >= 1.0));",mU=Xe({opSnippet:fU}),AU={kernelName:xu,backendName:"webgl",kernelFunc:mU},yU="return float(a >= 1.0 || b >= 1.0);",gU=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,wU=en({opSnippet:gU,packedOpSnippet:xU,dtype:"bool"}),bU={kernelName:wu,backendName:"webgl",kernelFunc:wU},_U=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,xU=en({opSnippet:yU,packedOpSnippet:gU,dtype:"bool"}),wU={kernelName:wu,backendName:"webgl",kernelFunc:xU},bU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3011,7 +3011,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},vU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},_U=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3073,7 +3073,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},kU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new vU(a.shape,s,i,o,l):new _U(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},IU={kernelName:bu,backendName:"webgl",kernelFunc:kU},NU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
+    `}},vU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new _U(a.shape,s,i,o,l):new bU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},kU={kernelName:bu,backendName:"webgl",kernelFunc:vU},IU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3128,14 +3128,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
       }
       setOutput(result);
       }
-    `}},SU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new NU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},TU={kernelName:Uh,backendName:"webgl",kernelFunc:SU};function CU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function k_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let b=nA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=pp(a,u,n);c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("max",c,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=aP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=CU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var EU={kernelName:Ns,backendName:"webgl",kernelFunc:k_},RU=Bb+`
+    `}},NU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new IU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},SU={kernelName:Uh,backendName:"webgl",kernelFunc:NU};function TU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function I_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[c[x]];let b=aA(g,a.shape,a.dtype,c,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=pp(a,c,n);u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("max",u,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,u),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=rP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=TU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var CU={kernelName:Ns,backendName:"webgl",kernelFunc:I_},EU=Vb+`
   return max(a, b);
-`,MU=`
+`,RU=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+hp+`
   return result;
-`,FU=en({opSnippet:RU,packedOpSnippet:MU,cpuKernelImpl:sP}),$U={kernelName:Ss,backendName:"webgl",kernelFunc:FU};function DU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return On({inputs:{x:a},backend:n});let h=new fc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var OU={kernelName:Ts,backendName:"webgl",kernelFunc:DU};function zU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new sA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var PU={kernelName:_u,backendName:"webgl",kernelFunc:zU},LU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,MU=en({opSnippet:EU,packedOpSnippet:RU,cpuKernelImpl:aP}),FU={kernelName:Ss,backendName:"webgl",kernelFunc:MU};function $U(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;kl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return On({inputs:{x:a},backend:n});let h=new fc(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var DU={kernelName:Ts,backendName:"webgl",kernelFunc:$U};function OU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new oA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var zU={kernelName:_u,backendName:"webgl",kernelFunc:OU},PU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3181,8 +3181,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},WU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
-      const ivec3 pads = ivec3(${u}, ${h}, ${d});
+    `}},LU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=`
+      const ivec3 pads = ivec3(${c}, ${h}, ${d});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -3218,7 +3218,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${c};
+            for (int wC = 0; wC < ${u};
                 wC += ${i}) {
               float dyC = float(dyCCorner + wC) / ${r}.0;
 
@@ -3235,8 +3235,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
               // Get the current value, check it against the value from the
               // position matrix.
               int curPosValue =
-                  wD * ${l} * ${c} +
-                  wR * ${c} + wC;
+                  wD * ${l} * ${u} +
+                  wR * ${u} + wC;
               float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
               dotProd += dyValue * mask;
@@ -3245,14 +3245,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function BU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new sA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new WU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var VU={kernelName:Hh,backendName:"webgl",kernelFunc:BU};function UU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;kl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new fc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new LU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var jU={kernelName:jh,backendName:"webgl",kernelFunc:UU};function HU(e,t,n,r){let a=new fc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new fc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var GU={kernelName:Gh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=R.computePool2DInfo(r.shape,a,s,c,i),[h,d]=HU(r,o,u,l);return[h,d]}};function qU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var XU={kernelName:Cs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[u[N]];let _=nA(w,r.shape,r.dtype,u,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=pp(r,u,i);p.push(f),c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=qU(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function KU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var ZU={kernelName:Es,backendName:"webgl",kernelFunc:KU},YU=Bb+`
+    `}};function WU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new oA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new LU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var BU={kernelName:Hh,backendName:"webgl",kernelFunc:WU};function VU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;kl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new fc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new PU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var UU={kernelName:jh,backendName:"webgl",kernelFunc:VU};function jU(e,t,n,r){let a=new fc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new fc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var HU={kernelName:Gh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,a,s,u,i),[h,d]=jU(r,o,c,l);return[h,d]}};function GU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=_i(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var qU={kernelName:Cs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[c[N]];let _=aA(w,r.shape,r.dtype,c,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=pp(r,c,i);p.push(f),u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("sum",u,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=GU(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function XU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=_i(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var KU={kernelName:Es,backendName:"webgl",kernelFunc:XU},ZU=Vb+`
   return min(a, b);
-`,JU=`
+`,YU=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+hp+`
   return result;
-`,QU=en({opSnippet:YU,packedOpSnippet:JU,cpuKernelImpl:iP}),ej={kernelName:Rs,backendName:"webgl",kernelFunc:QU},tj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ot(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
+`,JU=en({opSnippet:ZU,packedOpSnippet:YU,cpuKernelImpl:sP}),QU={kernelName:Rs,backendName:"webgl",kernelFunc:JU},ej=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=ot(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3281,7 +3281,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},nj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=dn("rc",r),l=dn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
+    `}},tj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=dn("rc",r),l=dn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3292,11 +3292,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
       `;d=`
         ${a} rc = outputLoc;
         ${p}
-        result[0] = getChannel(getX(${l.join()}), ${u});
+        result[0] = getChannel(getX(${l.join()}), ${c});
         ${o[r-1]} += 1;
-        if(${c}) {
+        if(${u}) {
           ${p}
-          result[1] = getChannel(getX(${l.join()}), ${u});
+          result[1] = getChannel(getX(${l.join()}), ${c});
         }
       `}else{let p=`
         ${a} source = rc;
@@ -3310,21 +3310,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
       `;d=`
         ${a} rc = outputLoc;
         ${p}
-        result[0] = getChannel(getX(${l.join()}), ${u});
+        result[0] = getChannel(getX(${l.join()}), ${c});
         ${o[r-1]} += 1;
-        if(${c}) {
+        if(${u}) {
           ${p}
-          result[1] = getChannel(getX(${l.join()}), ${u});
+          result[1] = getChannel(getX(${l.join()}), ${c});
         }
         rc = outputLoc;
         ${o[r-2]} += 1;
         if(${o[r-2]} < ${this.outputShape[r-2]}) {
           ${p}
-          result[2] = getChannel(getX(${l.join()}), ${u});
+          result[2] = getChannel(getX(${l.join()}), ${c});
           ${o[r-1]} += 1;
-          if(${c}) {
+          if(${u}) {
             ${p}
-            result[3] = getChannel(getX(${l.join()}), ${u});
+            result[3] = getChannel(getX(${l.join()}), ${c});
           }
         }
       `}this.userCode=`
@@ -3337,13 +3337,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},rj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nj(r.shape,a,s):new tj(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},aj={kernelName:vu,backendName:"webgl",kernelFunc:rj},sj=`if (b == 0.0) return NAN;
-  return mod(a, b);`,ij=`
+    `}},nj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tj(r.shape,a,s):new ej(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},rj={kernelName:vu,backendName:"webgl",kernelFunc:nj},aj=`if (b == 0.0) return NAN;
+  return mod(a, b);`,sj=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+hp+`
   return result;
-`,oj=en({opSnippet:sj,packedOpSnippet:ij}),lj={kernelName:No,backendName:"webgl",kernelFunc:oj},uj=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,ij=en({opSnippet:aj,packedOpSnippet:sj}),oj={kernelName:No,backendName:"webgl",kernelFunc:ij},lj=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3365,11 +3365,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YL=Xe({opSnippet:ZL}),JL={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},cj=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},uj=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,hj=`
+return a / b;`,cj=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3387,14 +3387,14 @@ return a / b;`,hj=`
   }
 
   return result;
-`,I_=en({opSnippet:cj,packedOpSnippet:hj,checkOutOfBounds:!0}),dj={kernelName:ys,backendName:"webgl",kernelFunc:I_},N_="return a - b;",S_=en({opSnippet:N_,packedOpSnippet:N_,supportsComplex:!0,cpuKernelImpl:fP}),pj={kernelName:Ks,backendName:"webgl",kernelFunc:S_};function T_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=k_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=S_({inputs:{a,b:c},backend:n}),h=x_({inputs:{x:u},backend:n}),d=aA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=I_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var fj={kernelName:qs,backendName:"webgl",kernelFunc:T_};function mj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:T_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new uj(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var Aj={kernelName:qh,backendName:"webgl",kernelFunc:mj},C_="return -x;";function yj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=lP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,C_):a=new La(r.shape,C_),n.runWebGLProgram(a,[r],r.dtype)}var gj={kernelName:So,backendName:"webgl",kernelFunc:yj},xj=Pr.nonMaxSuppressionV3Impl;function wj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=xj(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var bj={kernelName:Co,backendName:"webgl",kernelFunc:wj},_j=Pr.nonMaxSuppressionV4Impl;function vj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=_j(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var kj={kernelName:Eo,backendName:"webgl",kernelFunc:vj},Ij=Pr.nonMaxSuppressionV5Impl;function Nj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=Ij(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Sj={kernelName:Ro,backendName:"webgl",kernelFunc:Nj},Tj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,N_=en({opSnippet:uj,packedOpSnippet:cj,checkOutOfBounds:!0}),hj={kernelName:ys,backendName:"webgl",kernelFunc:N_},S_="return a - b;",T_=en({opSnippet:S_,packedOpSnippet:S_,supportsComplex:!0,cpuKernelImpl:pP}),dj={kernelName:Ks,backendName:"webgl",kernelFunc:T_};function C_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=I_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),c=T_({inputs:{a,b:u},backend:n}),h=w_({inputs:{x:c},backend:n}),d=iA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=N_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var pj={kernelName:qs,backendName:"webgl",kernelFunc:C_};function fj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:C_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new lj(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var mj={kernelName:qh,backendName:"webgl",kernelFunc:fj},E_="return -x;";function Aj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=oP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new El(r.shape,E_):a=new La(r.shape,E_),n.runWebGLProgram(a,[r],r.dtype)}var yj={kernelName:So,backendName:"webgl",kernelFunc:Aj},gj=Pr.nonMaxSuppressionV3Impl;function xj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=gj(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var wj={kernelName:Co,backendName:"webgl",kernelFunc:xj},bj=Pr.nonMaxSuppressionV4Impl;function _j(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=bj(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var vj={kernelName:Eo,backendName:"webgl",kernelFunc:_j},kj=Pr.nonMaxSuppressionV5Impl;function Ij(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=kj(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Nj={kernelName:Ro,backendName:"webgl",kernelFunc:Ij},Sj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${r}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},Cj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),c=new Tj(l,s,i,o),u=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},Ej={kernelName:Fs,backendName:"webgl",kernelFunc:Cj};function gp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=gp({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return uA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Rj={kernelName:Ko,backendName:"webgl",kernelFunc:gp};function E_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=E_({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return uA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Mj={kernelName:Mo,backendName:"webgl",kernelFunc:E_};function Fj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return lA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=lA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=u_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var $j={kernelName:Fo,backendName:"webgl",kernelFunc:Fj},Dj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ot(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
+    `}},Tj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new Sj(l,s,i,o),c=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},Cj={kernelName:Fs,backendName:"webgl",kernelFunc:Tj};function gp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=gp({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return hA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Ej={kernelName:Ko,backendName:"webgl",kernelFunc:gp};function R_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Ac({inputs:{input:r},backend:n}),s=R_({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Wa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return hA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Rj={kernelName:Mo,backendName:"webgl",kernelFunc:R_};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return cA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=cA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=c_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Fj={kernelName:Fo,backendName:"webgl",kernelFunc:Mj},$j=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=ot(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
         uniform float value;
@@ -3421,19 +3421,19 @@ return a / b;`,hj=`
           setOutput(getX(${o}));
         }
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},Oj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ot(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=dn("rc",r),l=dn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
-       if(${c}) {
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},Dj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ot(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=dn("rc",r),l=dn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
+       if(${u}) {
       `,r===1?"":`}
        rc = outputLoc;
        ${o[r-2]} += 1;
        if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":`  ${o[r-1]} += 1;
-         if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
+         if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
         ${h[f]}
         if (${d}) {
           result[${f}] = float(value);
         } else {
           ${a} source = rc - start;
-          result[${f}] = getChannel(getX(${l.join()}), ${u});
+          result[${f}] = getChannel(getX(${l.join()}), ${c});
         }
       `;p+=r===1?"} ":"}}",this.userCode=`
       const ${a} start = ${a}(${s});
@@ -3446,7 +3446,7 @@ return a / b;`,hj=`
         ${p}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},R_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Oj(a.shape,s,i):new Dj(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},zj={kernelName:$s,backendName:"webgl",kernelFunc:R_},Pj=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},M_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dj(a.shape,s,i):new $j(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},Oj={kernelName:$s,backendName:"webgl",kernelFunc:M_},zj=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3455,7 +3455,7 @@ return a / b;`,hj=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,Lj=`
+`,Pj=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3471,9 +3471,9 @@ return a / b;`,hj=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+hp+`
   return result;
-`,Wj=en({opSnippet:Pj,packedOpSnippet:Lj}),Bj={kernelName:Ds,backendName:"webgl",kernelFunc:Wj};function Vj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=v.parseAxisParam(s,a.shape),u=c,h=R.getAxesPermutation(u,o),d=a;h!=null&&(d=In({inputs:{x:a},backend:n,attrs:{perm:h}}),u=R.getInnerMostAxes(u.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=uP(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,u),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=sd(a.dtype),w=_i(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,c);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var Uj={kernelName:$o,backendName:"webgl",kernelFunc:Vj},M_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=cP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},jj={kernelName:ku,backendName:"webgl",kernelFunc:M_},Hj="return 1.0 / x;",Gj=Xe({opSnippet:Hj}),qj={kernelName:Do,backendName:"webgl",kernelFunc:Gj},Xj=gr+`
+`,Lj=en({opSnippet:zj,packedOpSnippet:Pj}),Wj={kernelName:Ds,backendName:"webgl",kernelFunc:Lj};function Bj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=R.getAxesPermutation(c,o),d=a;h!=null&&(d=In({inputs:{x:a},backend:n,attrs:{perm:h}}),c=R.getInnerMostAxes(c.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=lP(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=sd(a.dtype),w=_i(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var Vj={kernelName:$o,backendName:"webgl",kernelFunc:Bj},F_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=uP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},Uj={kernelName:ku,backendName:"webgl",kernelFunc:F_},jj="return 1.0 / x;",Hj=Xe({opSnippet:jj}),Gj={kernelName:Do,backendName:"webgl",kernelFunc:Hj},qj=gr+`
   return (x < 0.0) ? 0.0 : x;
-`,Kj=`
+`,Xj=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3483,9 +3483,9 @@ return a / b;`,hj=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Zj=Xe({opSnippet:Xj,packedOpSnippet:Kj}),Yj={kernelName:zs,backendName:"webgl",kernelFunc:Zj},Jj=gr+`
+`,Kj=Xe({opSnippet:qj,packedOpSnippet:Xj}),Zj={kernelName:zs,backendName:"webgl",kernelFunc:Kj},Yj=gr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,Qj=`
+`,Jj=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3495,10 +3495,10 @@ return a / b;`,hj=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,eH=Xe({opSnippet:Jj,packedOpSnippet:Qj}),tH={kernelName:Ls,backendName:"webgl",kernelFunc:eH},nH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,Qj=Xe({opSnippet:Yj,packedOpSnippet:Jj}),eH={kernelName:Ls,backendName:"webgl",kernelFunc:Qj},tH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${c[0]/u[0]},
-          ${c[1]/u[1]});
+          ${u[0]/c[0]},
+          ${u[1]/c[1]});
       const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
 
       void main() {
@@ -3528,11 +3528,11 @@ return a / b;`,hj=`
 
         setOutput(newValue);
       }
-    `}},rH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},nH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${c[0]/u[0]},
-          ${c[1]/u[1]},
-          ${c[1]/u[1]});
+          ${u[0]/c[0]},
+          ${u[1]/c[1]},
+          ${u[1]/c[1]});
       const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                      ${o}.0);
 
@@ -3605,7 +3605,7 @@ return a / b;`,hj=`
 
         setOutput(newValue);
       }
-    `}};function aH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rH(a.shape,l,c,s,i):new nH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var sH={kernelName:Ps,backendName:"webgl",kernelFunc:aH},iH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function rH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new nH(a.shape,l,u,s,i):new tH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var aH={kernelName:Ps,backendName:"webgl",kernelFunc:rH},sH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3615,8 +3615,8 @@ return a / b;`,hj=`
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${c});
-        const float widthScale = float(${u});
+        const float heightScale = float(${u});
+        const float widthScale = float(${c});
 
         const float invHeightScale = float(${h});
         const float invWidthScale = float(${d});
@@ -3686,10 +3686,10 @@ return a / b;`,hj=`
 
         setOutput(accumulator);
       }
-    `}};function oH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new iH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var lH={kernelName:Zh,backendName:"webgl",kernelFunc:oH},uH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function iH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new sH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var oH={kernelName:Zh,backendName:"webgl",kernelFunc:iH},lH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${c[0]/u[0]},
-          ${c[1]/u[1]});
+          ${u[0]/c[0]},
+          ${u[1]/c[1]});
       const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
 
       void main() {
@@ -3708,7 +3708,7 @@ return a / b;`,hj=`
 
         setOutput(newValue);
       }
-    `}};function cH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new uH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var hH={kernelName:Iu,backendName:"webgl",kernelFunc:cH},dH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function uH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new lH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var cH={kernelName:Iu,backendName:"webgl",kernelFunc:uH},hH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3718,8 +3718,8 @@ return a / b;`,hj=`
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${c});
-        const float widthScale = float(${u});
+        const float heightScale = float(${u});
+        const float widthScale = float(${c});
 
         const float invHeightScale = float(${h});
         const float invWidthScale = float(${d});
@@ -3778,7 +3778,7 @@ return a / b;`,hj=`
 
         setOutput(accumulator);
       }
-    `}};function pH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new dH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var fH={kernelName:Kh,backendName:"webgl",kernelFunc:pH},mH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function dH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new hH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var pH={kernelName:Kh,backendName:"webgl",kernelFunc:dH},fH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -3788,7 +3788,7 @@ return a / b;`,hj=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},AH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=dn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
+    `}},mH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=dn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3809,14 +3809,14 @@ return a / b;`,hj=`
             result.g = ${l(r.slice())};
           }
           if(${s}) {
-            result.b = ${c(r.slice())};
+            result.b = ${u(r.slice())};
             if(${a}) {
-              result.a = ${u(r.slice())};
+              result.a = ${c(r.slice())};
             }
           }
           setOutput(result);
         }
-    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function yH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return On({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AH(a.shape,o):new mH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var gH={kernelName:Ws,backendName:"webgl",kernelFunc:yH},xH=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function AH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return On({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mH(a.shape,o):new fH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var yH={kernelName:Ws,backendName:"webgl",kernelFunc:AH},gH=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         uniform vec4 params;
@@ -3836,7 +3836,7 @@ return a / b;`,hj=`
           }
           setOutput(outputValue);
         }
-    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},wH={kernelName:Zo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new xH(r.shape,s),[c,u]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},bH=`
+    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},xH={kernelName:Zo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new gH(r.shape,s),[u,c]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},wH=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3851,7 +3851,7 @@ return a / b;`,hj=`
       return base + 1.0;
     }
   }
-`,_H=Xe({opSnippet:bH}),vH={kernelName:Bs,backendName:"webgl",kernelFunc:_H},kH="return inversesqrt(x);",IH=Xe({opSnippet:kH,cpuKernelImpl:hP}),NH={kernelName:Vs,backendName:"webgl",kernelFunc:IH},F_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
+`,bH=Xe({opSnippet:wH}),_H={kernelName:Bs,backendName:"webgl",kernelFunc:bH},vH="return inversesqrt(x);",kH=Xe({opSnippet:vH,cpuKernelImpl:cP}),IH={kernelName:Vs,backendName:"webgl",kernelFunc:kH},$_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -3861,7 +3861,7 @@ return a / b;`,hj=`
           for (int i = 0; i < ${e}; i++) {
             int flattenedIndex = 0;
             for (int j = 0; j < ${t}; j++) {
-              int index = round(${u});
+              int index = round(${c});
               flattenedIndex += index * ${p};
             }
             if (flattenedIndex == coords[0]) {
@@ -3871,7 +3871,7 @@ return a / b;`,hj=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function SH(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=R.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new F_(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var TH={kernelName:zo,backendName:"webgl",kernelFunc:SH},CH=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ot(n);this.userCode=`
+      `}};function NH(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new $_(l,o,p.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var SH={kernelName:zo,backendName:"webgl",kernelFunc:NH},TH=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);r=o.join(),a=l.join()}let s=ot(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${r});
@@ -3881,21 +3881,21 @@ return a / b;`,hj=`
           setOutput(getB(${a}));
         }
       }
-    `}};function EH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new CH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],nr(a.dtype,s.dtype))}var RH={kernelName:Po,backendName:"webgl",kernelFunc:EH},MH=`
+    `}};function CH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new TH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],nr(a.dtype,s.dtype))}var EH={kernelName:Po,backendName:"webgl",kernelFunc:CH},RH=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${R.SELU_SCALEALPHA};
   float scale = ${R.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,FH=Xe({opSnippet:MH}),$H={kernelName:Lo,backendName:"webgl",kernelFunc:FH},DH="return 1.0 / (1.0 + exp(-1.0 * x));",OH=Xe({opSnippet:DH}),zH={kernelName:js,backendName:"webgl",kernelFunc:OH},PH=`
+`,MH=Xe({opSnippet:RH}),FH={kernelName:Lo,backendName:"webgl",kernelFunc:MH},$H="return 1.0 / (1.0 + exp(-1.0 * x));",DH=Xe({opSnippet:$H}),OH={kernelName:js,backendName:"webgl",kernelFunc:DH},zH=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,LH=Xe({opSnippet:PH}),WH={kernelName:Vo,backendName:"webgl",kernelFunc:LH},BH=Gb+`
+`,PH=Xe({opSnippet:zH}),LH={kernelName:Vo,backendName:"webgl",kernelFunc:PH},WH=qb+`
   return sin(x);
-`,VH=Xe({opSnippet:BH}),UH={kernelName:Us,backendName:"webgl",kernelFunc:VH},jH=`
+`,BH=Xe({opSnippet:WH}),VH={kernelName:Us,backendName:"webgl",kernelFunc:BH},UH=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,HH=Xe({opSnippet:jH}),GH={kernelName:Bo,backendName:"webgl",kernelFunc:HH},qH=`
+`,jH=Xe({opSnippet:UH}),HH={kernelName:Bo,backendName:"webgl",kernelFunc:jH},GH=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3915,9 +3915,9 @@ return a / b;`,hj=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,XH=Xe({opSnippet:qH}),KH={kernelName:Uo,backendName:"webgl",kernelFunc:XH},ZH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=R_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(u.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(u.shape,s,o,!1),f=xe({inputs:{x:u},backend:n,attrs:{shape:h}}),m=In({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},YH={kernelName:Nu,backendName:"webgl",kernelFunc:ZH};function JH(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new F_(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var QH={kernelName:Yh,backendName:"webgl",kernelFunc:JH};function eG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=mc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var tG={kernelName:jo,backendName:"webgl",kernelFunc:eG},nG="return sqrt(x);",rG=Xe({opSnippet:nG}),aG={kernelName:Hs,backendName:"webgl",kernelFunc:rG},sG="return x * x;",iG=Xe({opSnippet:sG}),oG={kernelName:Su,backendName:"webgl",kernelFunc:iG},$_="return (a - b) * (a - b);",lG=en({opSnippet:$_,packedOpSnippet:$_}),uG={kernelName:Xs,backendName:"webgl",kernelFunc:lG};function cG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=gr+`
+`,qH=Xe({opSnippet:GH}),XH={kernelName:Uo,backendName:"webgl",kernelFunc:qH},KH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],c=M_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(c.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(c.shape,s,o,!1),f=xe({inputs:{x:c},backend:n,attrs:{shape:h}}),m=In({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},ZH={kernelName:Nu,backendName:"webgl",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new $_(u,l,a.shape.length,s.shape.length,c,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var JH={kernelName:Yh,backendName:"webgl",kernelFunc:YH};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=mc({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,f})}var eG={kernelName:jo,backendName:"webgl",kernelFunc:QH},tG="return sqrt(x);",nG=Xe({opSnippet:tG}),rG={kernelName:Hs,backendName:"webgl",kernelFunc:nG},aG="return x * x;",sG=Xe({opSnippet:aG}),iG={kernelName:Su,backendName:"webgl",kernelFunc:sG},D_="return (a - b) * (a - b);",oG=en({opSnippet:D_,packedOpSnippet:D_}),lG={kernelName:Xs,backendName:"webgl",kernelFunc:oG};function uG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=gr+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new La(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var hG={kernelName:Na,backendName:"webgl",kernelFunc:cG},dG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
+  `,s=new La(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var cG={kernelName:Na,backendName:"webgl",kernelFunc:uG},hG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -3925,15 +3925,15 @@ return a / b;`,hj=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function pG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=ln.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=mc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=We(w.shape,w.dtype,x),T=pP(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new dG(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var fG={kernelName:Ho,backendName:"webgl",kernelFunc:pG},mG="return tan(x);",AG=Xe({opSnippet:mG}),yG={kernelName:Go,backendName:"webgl",kernelFunc:AG},gG=`
+    `}};function dG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=ln.sliceInfo(a.shape,s,i,o,l,u,c,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=mc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=We(w.shape,w.dtype,x),T=dP(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new hG(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var pG={kernelName:Ho,backendName:"webgl",kernelFunc:dG},fG="return tan(x);",mG=Xe({opSnippet:fG}),AG={kernelName:Go,backendName:"webgl",kernelFunc:mG},yG=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,xG=Xe({opSnippet:gG}),wG={kernelName:Zs,backendName:"webgl",kernelFunc:xG},_G=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=bG(e);this.userCode=`
+`,gG=Xe({opSnippet:yG}),xG={kernelName:Zs,backendName:"webgl",kernelFunc:gG},bG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=wG(e);this.userCode=`
       void main() {
         ${r} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function bG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function D_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>v.decodeString(u)),l=We(a.shape,a.dtype,o),c=mP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new _G(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var vG={kernelName:Ia,backendName:"webgl",kernelFunc:D_};function kG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=AP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var IG={kernelName:qo,backendName:"webgl",kernelFunc:kG},NG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+    `}};function wG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function O_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(c=>v.decodeString(c)),l=We(a.shape,a.dtype,o),u=fP(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new bG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var _G={kernelName:Ia,backendName:"webgl",kernelFunc:O_};function vG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=mP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var kG={kernelName:qo,backendName:"webgl",kernelFunc:vG},IG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4045,7 +4045,7 @@ return a / b;`,hj=`
               }
               setOutput(outputValue);
             }
-        `}};function SG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=r,[u,h,d,p]=a.shape,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=new NG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var TG={kernelName:Jh,backendName:"webgl",kernelFunc:SG};function CG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;kl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=yP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var EG={kernelName:Qh,backendName:"webgl",kernelFunc:CG};function RG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=mc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=xe({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var MG={kernelName:Xo,backendName:"webgl",kernelFunc:RG},FG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
+        `}};function NG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=new IG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var SG={kernelName:Jh,backendName:"webgl",kernelFunc:NG};function TG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;kl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=AP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var CG={kernelName:Qh,backendName:"webgl",kernelFunc:TG};function EG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==s&&(u[c++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=mc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=xe({inputs:{x:A},backend:n,attrs:{shape:u}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var RG={kernelName:Xo,backendName:"webgl",kernelFunc:EG},MG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,h=`
         sumValue += dot(values, segFilter);
     `,d="";a%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4078,7 +4078,7 @@ return a / b;`,hj=`
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${c}; i += 4) {
+        for (int i = 0; i < ${u}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -4097,8 +4097,8 @@ return a / b;`,hj=`
           ${h}
         }
 
-        int inIdx = inOffset + ${c};
-        if (${u===1}) {
+        int inIdx = inOffset + ${u};
+        if (${c===1}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             initializationValue,
@@ -4116,7 +4116,7 @@ return a / b;`,hj=`
           );
 
           ${h}
-        } else if (${u===2}) {
+        } else if (${c===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -4132,7 +4132,7 @@ return a / b;`,hj=`
           );
 
           ${h}
-        } else if (${u===3}) {
+        } else if (${c===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -4151,26 +4151,26 @@ return a / b;`,hj=`
         }
         setOutput(${l});
       }
-    `}};function $G(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=R.getAxesPermutation([c],o),h=a;u!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=R.getInnerMostAxes(1,o)[0]);let d=R.segment_util.computeOutShape(h.shape,c,i),p=v.sizeFromShape([h.shape[c]]),f=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=sd(a.dtype),A=(b,_,x,N,T)=>{let C=b.shape[0],F=b.shape[1],P=R.segment_util.segOpComputeOptimalWindowSize(F,T),W={windowSize:P,inSize:F,batchSize:C,numSegments:T},V=new FG(W,_),U=n.compileAndRun(V,[b,x],N);if(l.push(U),U.shape[1]===T)return U;let H=M_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=D_({inputs:{x:H},backend:n,attrs:{reps:[F/P]}});return l.push(H),l.push(X),A(U,_,X,N,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=xe({inputs:{x:y},backend:n,attrs:{shape:d}}),w=g;if(u!=null){l.push(g);let b=R.getUndoAxesPermutation(u);w=In({inputs:{x:w},backend:n,attrs:{perm:b}})}return 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function R7(e,t){return dte(e,t,"classWeight")}async function M7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());_e(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)v.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)v.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function F7(e,t,n){if(n instanceof Ve)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function mte(e){if(e.length===3)throw new $e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function yte(e,t,n){let r=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Ci(c),h=Rc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=pte(f,a[p]));let m=vt(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=vt(m(r[A],h[A]))}Ut(f),s.push(f)}return d=vt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Ci(s),o=Rc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=vt(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=vt(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return bte(this,e,t,n)}async fitDataset(e,t){return yte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return _e(s),Sn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ua(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ua(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ua(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)a[s]=ki(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=wn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await wn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Nte,generatedBy:`TensorFlow.js tfjs-layers v${XA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await wn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=wn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let 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'${e}'`);e=n[0]}return Tte(e,void 0,t)}async function Tte(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=kr(Ec(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};oy.className="ThresholdedReLU";re.registerClass(oy);var ly=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ey().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ly.className="Softmax";re.registerClass(ly);function Vl(e,t,n){if(typeof e=="number")return vi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Ir(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ua([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function uy(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function nv(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function zte(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=wr()),St(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=fd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function rv(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=uy(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=za.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function Pte(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=nv(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Jf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var cy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",cy.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Vl(t.kernelSize,e,"kernelSize"),this.strides=Vl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Kn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=Ga(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=Vl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!gA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ha(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},$c=class extends cy{constructor(e,t){super(e,t);this.kernel=null,$c.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=V3(this.activation.getClassName());if(a!=null&&this.rank===2)n=rv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=zte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=rv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Pte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Ir(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Lt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Dc=class extends $c{constructor(e){super(2,e);Dc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!gA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Dc.className="Conv2D";re.registerClass(Dc);var Xp=class extends $c{constructor(e){super(3,e);Xp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xp.className="Conv3D";re.registerClass(Xp);var hy=class extends Dc{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qp(o,h,c,this.padding),f=qp(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=md(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qp(t[r],o,s,this.padding),t[a]=qp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};hy.className="Conv2DTranspose";re.registerClass(hy);var av=class extends $c{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Gt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=Am(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseConstraint),e.pointwiseConstraint=Lt(this.pointwiseConstraint),e}};av.className="SeparableConv";var dy=class extends av{constructor(e){super(2,e)}};dy.className="SeparableConv2D";re.registerClass(dy);var Kp=class extends $c{constructor(e){super(1,e);Kp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!gA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv1D";re.registerClass(Kp);var py=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ip(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};py.className="Cropping2D";re.registerClass(py);var fy=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};fy.className="UpSampling2D";re.registerClass(fy);function Lte(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=wr()),St(a);let i=uy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=cl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var my=class extends cy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=lt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=Lte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ir(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseRegularizer),e}};my.className="DepthwiseConv2D";re.registerClass(my);function sv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function iv(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(_r(2,l));if(t=nt(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Yt(a,-1)),a=nt(a,c)),r&&(t=$n(t,0),a!=null&&(a=$n(a,0)));let u=[],h,d=n,p=t.shape[0],f=sr(t),m;a!=null&&(m=sr(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=Fn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((C,F)=>w[1][F].mul(_).add(C.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&u.push(h)}let A;return o&&(A=un(u,1)),[h,A,d]})}var jr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Gt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return _r(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){PA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");PA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Gt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Gt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ut(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=sv(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Gt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof vr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=iv((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Ct(e.shape);return t=Ce(t,[1,2]),t=Ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?kA(t,[1,n]):t):this.cell.stateSize>1?[kA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===jr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=kr(r,n);return new e(Object.assign(t,{cell:a}))}};jr.className="RNN";re.registerClass(jr);var Tc=class extends qe{},Yp=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(O(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=O(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";re.registerClass(Yp);var Ay=class extends jr{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Ay.className="SimpleRNN";re.registerClass(Ay);var Jp=class extends Tc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let c=Vr(e,this.kernel.read());this.useBias&&(c=Ur(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=zt(u,[2*this.units,this.units],u.rank-1),p=Vr(r,h),[f,m,A]=zt(c,3,c.rank-1),[y,g]=zt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Vr(O(o,r),d);l=this.activation.apply(se(A,w));let b=se(O(i,r),O(se(1,_t(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Jp.className="GRUCell";re.registerClass(Jp);var yy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};yy.className="GRU";re.registerClass(yy);var Oc=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends lr{apply(i,o){let l=a.apply([s]),c=new Sp().apply([s]),u=a.apply([s*2]);return J3(J3(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=zt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(O(l,a),O(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=O(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Oc.className="LSTMCell";re.registerClass(Oc);var gy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Oc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};gy.className="LSTM";re.registerClass(gy);var Zp=class extends Tc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){PA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ni(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(kr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return LA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}WA(t)}};Zp.className="StackedRNNCells";re.registerClass(Zp);function qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>e7(t(),n),i=()=>Sc(s,t,r);return!a||a<=1?Ut(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ut(o.clone()))}var Wte=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},ov=class extends jr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Gt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Ir(l,r[0],a,s[0],i[0]),h=Ir(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};ov.className="ConvRNN2D";var Qp=class extends Oc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Vl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Vl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",Kn(this.padding),this.dataFormat=s||"channelsLast",St(this.dataFormat),this.dilationRate=Vl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=lt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends lr{apply(u,h){let d=l.apply([c]),p=Or([c]),f=l.apply([c*2]);return NA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:O(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=zt(this.kernel.read(),i,g),[N,T,C,F]=this.useBias?zt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,N,this.padding),u=this.inputConv(u,b,T,this.padding),h=this.inputConv(h,_,C,this.padding),d=this.inputConv(d,x,F,this.padding);let[P,W,V,U]=zt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,P),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(O(X,s),O(H,this.activation.apply(se(h,A)))),ee=O(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Wte(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=na(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return na(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var xy=class extends ov{constructor(e){let t=new Qp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};xy.className="ConvLSTM2D";re.registerClass(xy);var e0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Sc(()=>e7(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};e0.className="Dropout";re.registerClass(e0);var wy=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};wy.className="SpatialDropout1D";re.registerClass(wy);var by=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=V3(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};by.className="Dense";re.registerClass(by);var _y=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Va(e,1)]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return hQ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};_y.className="Flatten";re.registerClass(_y);var vy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ga(e.activation)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ha(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Activation";re.registerClass(vy);var ky=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return z(()=>(e=Pe(e),uQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};ky.className="RepeatVector";re.registerClass(ky);var Iy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Va(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Reshape";re.registerClass(Iy);var Ny=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=_r(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Gt({ndim:this.dims.length+1})]}computeOutputShape(e){e=lt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Permute";re.registerClass(Ny);var Sy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Wu(di(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Wu(di(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Sy.className="Masking";re.registerClass(Sy);var Ty=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Ht(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ht(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=Pe(e),di(e,je(e))):null)}computeOutputShape(e){if(e=lt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=kc(n,"int32")),Q3(this.embeddings.read(),n.as1D()).reshape(lt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Lt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="Embedding";re.registerClass(Ty);var Ri=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[lt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ba(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ba(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return z(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ua(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Ic(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Va(c.slice(1))));d=nt(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=_r(1,l).concat([0]);n.push(nt(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=nt(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(_r(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ba(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return z(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Yt(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=ar(n,t[r]);return n})}},Cy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};Cy.className="Add";re.registerClass(Cy);var Ey=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=O(t,e[n]);return t})}};Ey.className="Multiply";re.registerClass(Ey);var Ry=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return O(1/e.length,t)})}};Ry.className="Average";re.registerClass(Ry);var My=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Dr(t,e[n]);return t})}};My.className="Maximum";re.registerClass(My);var Fy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return z(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ml(t,e[n]);return t})}};Fy.className="Minimum";re.registerClass(Fy);var $y=class extends Ri{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(v.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return z(()=>NA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return z(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(Fn(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Yt(t[s],-1)):r.push(t[s]);let a=rt(r,this.axis);return dd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Concatenate";re.registerClass($y);function zc(e,t){for(;e<0;)e+=t;return e}function Bte(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Dy=class extends Ri{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>zc(a,e[s].shape.length)):r=[zc(this.axes,t.shape.length),zc(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,r[0]),n=Lp(n,r[1])),Bte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[zc(this.axes,e.length),zc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Dy.className="Dot";re.registerClass(Dy);var Oy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Sc(()=>Np(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Oy.className="GaussianNoise";re.registerClass(Oy);var zy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Sc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Np(n.shape,1,r))},()=>n,t.training||!1):n})}};zy.className="GaussianDropout";re.registerClass(zy);var Py=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Sc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Da(Al(n),this.rate);o=kc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Pe(e),t.training||!1)}return e})}};Py.className="AlphaDropout";re.registerClass(Py);function Pc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=J5(e,t,n,r,a,s);else if(e.rank===3)i=Q5(e,t,n,r,a,s);else if(e.rank===4)i=ex(e,t,n,r,a,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Vte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance;return[Pc(e,i,o,n,t,a),i,o]})}function Ute(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance,l=[];for(let p of _r(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Pc(e,c,u,d,h,a),i,o]})}function jte(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),_r(0,e.rank-1))?Vte(e,t,n,r,a):Ute(e,t,n,r,a)}var Ly=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=lt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Gt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=_r(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=vi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!v.arraysEqual(c,_r(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Pc(r,A,y,g,w,this.epsilon)}else return Pc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=jte(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{z(()=>{let w=1-g,b=A.read(),_=b.sub(y).mul(w);A.write(b.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Lt(this.betaConstraint),gammaConstraint:Lt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="BatchNormalization";re.registerClass(Ly);var Wy=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=lt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ba(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=kd(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Pc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="LayerNormalization";re.registerClass(Wy);function Hte(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=wr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=Ku(e,t,n,o):i=Vu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function lv(e,t,n,r,a,s){return z(()=>{St(a),G3(s),Kn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=wr()),s==null&&(s="max"),e=nv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=um(e,t,n,o):i=Kf(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var uv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Kn(this.padding),this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){e=lt(e);let t=Ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Ic(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Oa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends uv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};Vy.className="MaxPooling1D";re.registerClass(Vy);var Uy=class extends uv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"avg")}};Uy.className="AveragePooling1D";re.registerClass(Uy);var cv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};jy.className="MaxPooling2D";re.registerClass(jy);var Hy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"avg")}};Hy.className="AveragePooling2D";re.registerClass(Hy);var hv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:5})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),r=Ir(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),lv(e,t,n,r,a,"max")}};Gy.className="MaxPooling3D";re.registerClass(Gy);var qy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),lv(e,t,n,r,a,"avg")}};qy.className="AveragePooling3D";re.registerClass(qy);var dv=class extends qe{constructor(e){super(e);this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Xy=class extends dv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vt(n,1)})}};Xy.className="GlobalAveragePooling1D";re.registerClass(Xy);var Ky=class extends dv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vn(n,1)})}};Ky.className="GlobalMaxPooling1D";re.registerClass(Ky);var pv=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Zy=class extends pv{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?vt(n,[1,2]):vt(n,[2,3])})}};Zy.className="GlobalAveragePooling2D";re.registerClass(Zy);var Yy=class extends pv{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?vn(n,[1,2]):vn(n,[2,3])})}};Yy.className="GlobalMaxPooling2D";re.registerClass(Yy);var fv=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=kr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Jy=class extends fv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=lt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=lt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=Pe(e),iv((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Jy.className="TimeDistributed";re.registerClass(Jy);function Gte(e){Ii(aQ,"BidirectionalMergeMode",e)}var qte="concat",Qy=class extends fv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=kr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=kr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?qte:e.mergeMode,Gte(this.mergeMode),e.weights)throw new $e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Sn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=sv(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new 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i;return this.mergeMode==="concat"?i=NA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=O(.5,se(r,a)):this.mergeMode==="mul"?i=O(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ni(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ni(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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vne=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}],Ev={};Oe(Ev,{json:()=>kne});var kne=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}],Rv={};Oe(Rv,{json:()=>Ine});var 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function M7(e,t){return hte(e,t,"classWeight")}async function F7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());_e(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let p=0;p<this.inputs.length;++p)u.push({key:this.inputs[p],value:n[p]});let c=new Ci(u),h=Rc(this.outputs,c,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=dte(f,a[p]));let m=vt(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=vt(m(r[A],h[A]))}Ut(f),s.push(f)}return d=vt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Ci(s),o=Rc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=vt(u(a[l],o[l]));l===0?n=c:n=se(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],h=vt(u(a[c],o[c]));t.push(h)}return t})}async fit(e,t,n={}){return wte(this,e,t,n)}async fitDataset(e,t){return Ate(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return _e(s),Sn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ua(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ua(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ua(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)a[s]=ki(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=wn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await wn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Ite,generatedBy:`TensorFlow.js tfjs-layers v${ZA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await wn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=wn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;T7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){T7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ca.className="Model";re.registerClass(ca);var B7=class extends ca{};B7.className="Functional";re.registerClass(B7);async function Nte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Ec(n),a=kr(r,t);if(e.weightsManifest!=null){let s=await wn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),_e(s)}return a}async function Tte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=wn.getLoadHandlers(e,t);if(n.length===0)n.push(wn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Ste(e,void 0,t)}async function Ste(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=kr(Ec(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};uy.className="ThresholdedReLU";re.registerClass(uy);var cy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ny().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Softmax";re.registerClass(cy);function Vl(e,t,n){if(typeof e=="number")return vi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Ir(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ua([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function hy(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function rv(e,t){return z(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function Ote(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=wr()),St(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=fd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function av(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=hy(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=za.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function zte(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=wr()),St(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=rv(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=em(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var dy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",dy.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Vl(t.kernelSize,e,"kernelSize"),this.strides=Vl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Kn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=Ga(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=Vl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!wA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ha(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},$c=class extends dy{constructor(e,t){super(e,t);this.kernel=null,$c.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=U3(this.activation.getClassName());if(a!=null&&this.rank===2)n=av(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Ote(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=av(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=zte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Ir(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Lt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Dc=class extends $c{constructor(e){super(2,e);Dc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!wA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Dc.className="Conv2D";re.registerClass(Dc);var Xp=class extends $c{constructor(e){super(3,e);Xp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xp.className="Conv3D";re.registerClass(Xp);var py=class extends Dc{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qp(o,h,u,this.padding),f=qp(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=md(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qp(t[r],o,s,this.padding),t[a]=qp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};py.className="Conv2DTranspose";re.registerClass(py);var sv=class extends $c{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Gt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=gm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseConstraint),e.pointwiseConstraint=Lt(this.pointwiseConstraint),e}};sv.className="SeparableConv";var fy=class extends sv{constructor(e){super(2,e)}};fy.className="SeparableConv2D";re.registerClass(fy);var Kp=class extends $c{constructor(e){super(1,e);Kp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!wA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv1D";re.registerClass(Kp);var my=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ip(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};my.className="Cropping2D";re.registerClass(my);var Ay=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,aQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="UpSampling2D";re.registerClass(Ay);function Pte(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=wr()),St(a);let i=hy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=cl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var yy=class extends dy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=lt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=Pte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ir(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseRegularizer),e}};yy.className="DepthwiseConv2D";re.registerClass(yy);function iv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function ov(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(_r(2,l));if(t=nt(t,u),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Yt(a,-1)),a=nt(a,u)),r&&(t=$n(t,0),a!=null&&(a=$n(a,0)));let c=[],h,d=n,p=t.shape[0],f=sr(t),m;a!=null&&(m=sr(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=Fn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((C,F)=>w[1][F].mul(_).add(C.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=un(c,1)),[h,A,d]})}var jr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Gt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return _r(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){WA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");WA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Gt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Gt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ut(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=iv(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Gt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof vr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=ov((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return z(()=>{let t=Ct(e.shape);return t=Ce(t,[1,2]),t=Ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?NA(t,[1,n]):t):this.cell.stateSize>1?[NA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===jr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=kr(r,n);return new e(Object.assign(t,{cell:a}))}};jr.className="RNN";re.registerClass(jr);var Tc=class extends qe{},Yp=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(O(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=O(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";re.registerClass(Yp);var gy=class extends jr{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};gy.className="SimpleRNN";re.registerClass(gy);var Jp=class extends Tc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let u=Vr(e,this.kernel.read());this.useBias&&(u=Ur(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let c=this.recurrentKernel.read(),[h,d]=zt(c,[2*this.units,this.units],c.rank-1),p=Vr(r,h),[f,m,A]=zt(u,3,u.rank-1),[y,g]=zt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Vr(O(o,r),d);l=this.activation.apply(se(A,w));let b=se(O(i,r),O(se(1,_t(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Jp.className="GRUCell";re.registerClass(Jp);var xy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jp(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};xy.className="GRU";re.registerClass(xy);var Oc=class extends Tc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ga(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=zl([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,Ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends lr{apply(i,o){let l=a.apply([s]),u=new Sp().apply([s]),c=a.apply([s*2]);return Q3(Q3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=zt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),u=se(O(l,a),O(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let A=O(c,this.activation.apply(u));return[A,A,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ha(this.activation),recurrentActivation:Ha(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Oc.className="LSTMCell";re.registerClass(Oc);var wy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Oc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};wy.className="LSTM";re.registerClass(wy);var Zp=class extends Tc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){WA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ni(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(kr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return BA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}VA(t)}};Zp.className="StackedRNNCells";re.registerClass(Zp);function qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>t7(t(),n),i=()=>Sc(s,t,r);return!a||a<=1?Ut(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ut(o.clone()))}var Lte=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},lv=class extends jr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Gt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=Ir(l,r[0],a,s[0],i[0]),h=Ir(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};lv.className="ConvRNN2D";var Qp=class extends Oc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Vl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Vl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",Kn(this.padding),this.dataFormat=s||"channelsLast",St(this.dataFormat),this.dilationRate=Vl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=lt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends lr{apply(c,h){let d=l.apply([u]),p=Or([u]),f=l.apply([u*2]);return TA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qa({ones:()=>Fn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:O(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qa({ones:()=>Fn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=zt(this.kernel.read(),i,g),[N,T,C,F]=this.useBias?zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,w,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,_,C,this.padding),d=this.inputConv(d,x,F,this.padding);let[P,W,V,U]=zt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,P),m=this.recurrentConv(m,W),A=this.recurrentConv(A,V),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(se(u,f)),X=this.recurrentActivation.apply(se(c,m)),G=se(O(X,s),O(j,this.activation.apply(se(h,A)))),ee=O(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Lte(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=na(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return na(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var by=class extends lv{constructor(e){let t=new Qp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};by.className="ConvLSTM2D";re.registerClass(by);var e0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Sc(()=>t7(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};e0.className="Dropout";re.registerClass(e0);var _y=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};_y.className="SpatialDropout1D";re.registerClass(_y);var vy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=Ga(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=U3(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ha(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Dense";re.registerClass(vy);var ky=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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z(()=>(e=Pe(e),lQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="RepeatVector";re.registerClass(Ny);var Sy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Va(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Reshape";re.registerClass(Sy);var Ty=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=_r(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Gt({ndim:this.dims.length+1})]}computeOutputShape(e){e=lt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="Permute";re.registerClass(Ty);var Cy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Wu(di(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Wu(di(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Cy.className="Masking";re.registerClass(Cy);var Ey=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Ht(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ht(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=Pe(e),di(e,je(e))):null)}computeOutputShape(e){if(e=lt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=kc(n,"int32")),e7(this.embeddings.read(),n.as1D()).reshape(lt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Lt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Embedding";re.registerClass(Ey);var Ri=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[lt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ba(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. 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Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return z(()=>TA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return z(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(Fn(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Yt(t[s],-1)):r.push(t[s]);let a=rt(r,this.axis);return dd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Concatenate";re.registerClass(Oy);function zc(e,t){for(;e<0;)e+=t;return e}function Wte(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c<l+i;++c)u.push(c);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var zy=class extends Ri{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>zc(a,e[s].shape.length)):r=[zc(this.axes,t.shape.length),zc(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,r[0]),n=Lp(n,r[1])),Wte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[zc(this.axes,e.length),zc(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Dot";re.registerClass(zy);var Py=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Sc(()=>Np(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Py.className="GaussianNoise";re.registerClass(Py);var Ly=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Sc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Np(n.shape,1,r))},()=>n,t.training||!1):n})}};Ly.className="GaussianDropout";re.registerClass(Ly);var Wy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Sc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Da(Al(n),this.rate);o=kc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Pe(e),t.training||!1)}return e})}};Wy.className="AlphaDropout";re.registerClass(Wy);function Pc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=Q5(e,t,n,r,a,s);else if(e.rank===3)i=ex(e,t,n,r,a,s);else if(e.rank===4)i=tx(e,t,n,r,a,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Bte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance;return[Pc(e,i,o,n,t,a),i,o]})}function Vte(e,t,n,r,a=.001){return z(()=>{let s=kd(e,r),i=s.mean,o=s.variance,l=[];for(let p of _r(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Pc(e,u,c,d,h,a),i,o]})}function Ute(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),_r(0,e.rank-1))?Bte(e,t,n,r,a):Vte(e,t,n,r,a)}var By=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=lt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Gt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=_r(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=vi(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,_r(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Pc(r,A,y,g,w,this.epsilon)}else return 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extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=lt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ba(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=kd(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),c=c.tile(p),h=h.tile(p),Pc(n,i,o,h,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="LayerNormalization";re.registerClass(Vy);function jte(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=wr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=Ku(e,t,n,o):i=Vu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function uv(e,t,n,r,a,s){return z(()=>{St(a),q3(s),Kn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=wr()),s==null&&(s="max"),e=rv(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=hm(e,t,n,o):i=Yf(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var cv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Kn(this.padding),this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){e=lt(e);let t=Ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Ic(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Oa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};jy.className="MaxPooling1D";re.registerClass(jy);var Hy=class extends cv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"max")}};Gy.className="MaxPooling2D";re.registerClass(Gy);var qy=class extends hv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),t0(e,t,n,r,a,"avg")}};qy.className="AveragePooling2D";re.registerClass(qy);var dv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:5})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),r=Ir(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Xy=class extends dv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),uv(e,t,n,r,a,"max")}};Xy.className="MaxPooling3D";re.registerClass(Xy);var Ky=class extends dv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),uv(e,t,n,r,a,"avg")}};Ky.className="AveragePooling3D";re.registerClass(Ky);var pv=class extends qe{constructor(e){super(e);this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Zy=class extends pv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vt(n,1)})}};Zy.className="GlobalAveragePooling1D";re.registerClass(Zy);var Yy=class extends pv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return vn(n,1)})}};Yy.className="GlobalMaxPooling1D";re.registerClass(Yy);var fv=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jy=class extends fv{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?vt(n,[1,2]):vt(n,[2,3])})}};Jy.className="GlobalAveragePooling2D";re.registerClass(Jy);var Qy=class extends fv{call(e,t){return z(()=>{let n=Pe(e);return 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e(s)}},e2=class extends mv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=lt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=lt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=Pe(e),ov((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};e2.className="TimeDistributed";re.registerClass(e2);function Hte(e){Ii(rQ,"BidirectionalMergeMode",e)}var Gte="concat",t2=class extends mv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=kr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=kr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Gte:e.mergeMode,Hte(this.mergeMode),e.weights)throw new $e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Sn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=iv(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new 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i;return this.mergeMode==="concat"?i=TA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=O(.5,se(r,a)):this.mergeMode==="mul"?i=O(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ni(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ni(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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vne=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}],Mv={};Oe(Mv,{json:()=>kne});var 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Rne=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],Wv={};Oe(Wv,{json:()=>Mne});var Mne=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],Vv=class{static 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d={};e.library!=null&&e.library.function!=null&&(d=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let p={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:d};return s.length>0&&(p.initNodes=s),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=vv(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(r=>r.startsWith("^")?r.substr(1):r),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((r,a)=>(r[a.name]={type:a.type,inputIndexStart:a.start,inputIndexEnd:a.end},r),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((r,a)=>{let s=a.type,i;switch(a.type){case"string":i=r2(e.attr,a.tfName,a.defaultValue),i===void 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s=[],i=[];e.signature.inputArg.forEach(u=>{let[c]=ha(u.name),h={name:c,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:i2(u.type),type:"dtype"}},children:[]};h.signatureKey=u.name,s.push(h),a[c]=h}),Object.keys(a).forEach(u=>{let c=a[u];c.inputNames.forEach(h=>{let[d]=ha(h);c.inputs.push(a[d]),a[d].children.push(c)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[c,h]=ha(o[u.name]),d=a[c];d!=null&&(d.defaultOutput=h,i.push(d))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:r,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function Fne(e){let t=J().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function Uv(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Fne(e);return t?n:n.toLowerCase()}function r2(e,t,n,r=!1){let a=e[t];return a!=null?Uv(a.s,r):n}function a2(e,t,n){let r=e[t];return r?r.b:n}function s2(e,t,n){let r=e[t]||{},a=r.i!=null?r.i:r.f!=null?r.f:n;return typeof a=="number"?a:parseInt(a,10)}function i2(e){switch(typeof e=="string"&&(e=Nr[e]),e){case Nr.DT_FLOAT:return"float32";case Nr.DT_INT32:case Nr.DT_INT64:case Nr.DT_INT8:case Nr.DT_UINT8:return"int32";case Nr.DT_BOOL:return"bool";case Nr.DT_DOUBLE:return"float32";case Nr.DT_STRING:return"string";default:return null}}function Bv(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function o2(e,t,n){let r=e[t];return r&&r.type?i2(r.type):n}function l2(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>i2(a)):n}function jv(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function u2(e,t,n){let r=e[t];return r&&r.shape?jv(r.shape):n}function c2(e,t,n){let r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(a=>typeof a=="number"?a:parseInt(a,10)):n}function h2(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>Uv(s,r)):n}function d2(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>jv(a)):n}function p2(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var $ne=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(r=>this.getInput(r)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((r,a)=>(r[a]=this.getAttr(a),r),{}))}getInput(e){return Cn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Cn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return s2(this.node.rawAttrs,e,t);if(n.s!=null)return r2(this.node.rawAttrs,e,t);if(n.b!=null)return a2(this.node.rawAttrs,e,t);if(n.shape!=null)return u2(this.node.rawAttrs,e,t);if(n.type!=null)return o2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return c2(this.node.rawAttrs,e,t);if(n.list.s!=null)return h2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return d2(this.node.rawAttrs,e,t);if(n.list.b!=null)return p2(this.node.rawAttrs,e,t);if(n.list.type!=null)return l2(this.node.rawAttrs,e,t)}return t}},Dne=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(k("a",e,t,n),k("b",e,t,n))];case"AddN":return[Ra(k("tensors",e,t,n))];case"FloorMod":case"Mod":return[pm(k("a",e,t,n),k("b",e,t,n))];case"Mul":return[O(k("a",e,t,n),k("b",e,t,n))];case"RealDiv":case"Div":return[me(k("a",e,t,n),k("b",e,t,n))];case"DivNoNan":return[rm(k("a",e,t,n),k("b",e,t,n))];case"FloorDiv":return[hd(k("a",e,t,n),k("b",e,t,n))];case"Sub":return[Ae(k("a",e,t,n),k("b",e,t,n))];case"Minimum":return[ml(k("a",e,t,n),k("b",e,t,n))];case"Maximum":return[Dr(k("a",e,t,n),k("b",e,t,n))];case"Pow":return[aa(k("a",e,t,n),k("b",e,t,n))];case"SquaredDifference":return[Dd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},One=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Dt(k("x",e,t,n))];case"Acos":return[Bf(k("x",e,t,n))];case"Acosh":return[Vf(k("x",e,t,n))];case"Asin":return[jf(k("x",e,t,n))];case"Asinh":return[Hf(k("x",e,t,n))];case"Atan":return[Gf(k("x",e,t,n))];case"Atan2":return[qf(k("x",e,t,n),k("y",e,t,n))];case"Atanh":return[Xf(k("x",e,t,n))];case"Ceil":return[Jf(k("x",e,t,n))];case"Complex":return[Sa(k("real",e,t,n),k("imag",e,t,n))];case"Cos":return[Hu(k("x",e,t,n))];case"Cosh":return[Ad(k("x",e,t,n))];case"Elu":return[hl(k("x",e,t,n))];case"Erf":return[am(k("x",e,t,n))];case"Exp":return[Gn(k("x",e,t,n))];case"Expm1":return[sm(k("x",e,t,n))];case"Floor":return[dl(k("x",e,t,n))];case"Log":return[Mn(k("x",e,t,n))];case"Log1p":return[wd(k("x",e,t,n))];case"Imag":return[gd(k("x",e,t,n))];case"Neg":return[_t(k("x",e,t,n))];case"Reciprocal":return[Am(k("x",e,t,n))];case"Real":return[Ju(k("x",e,t,n))];case"Relu":return[zr(k("x",e,t,n))];case"Round":return[ym(k("x",e,t,n))];case"Selu":return[Cd(k("x",e,t,n))];case"Sigmoid":return[Rn(k("x",e,t,n))];case"Sin":return[Ed(k("x",e,t,n))];case"Sign":return[xm(k("x",e,t,n))];case"Sinh":return[Rd(k("x",e,t,n))];case"Softplus":return[pl(k("x",e,t,n))];case"Sqrt":return[Jt(k("x",e,t,n))];case"Square":return[it(k("x",e,t,n))];case"Tanh":return[ll(k("x",e,t,n))];case"Tan":return[_m(k("x",e,t,n))];case"ClipByValue":return[bn(k("x",e,t,n),k("clipValueMin",e,t,n),k("clipValueMax",e,t,n))];case"Relu6":return[Sd(k("x",e,t,n))];case"Rsqrt":return[Td(Cn(e.inputNames[0],t,n))];case"Prod":return[Id(k("x",e,t,n),k("axes",e,t,n))];case"LeakyRelu":return[qu(k("x",e,t,n),k("alpha",e,t,n))];case"Prelu":return[Yu(k("x",e,t,n),k("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function cr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let r=0;r<e.length;r++){let a=e[r],s=t[r];v.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Hv(e){return!(typeof e=="number"||e.some(t=>t<0))}function Lc(e,t,n){let r=f2(e,n),a=!Hv(r);if(a&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(a&&t.forEach(s=>{r=f2(s.shape,r)}),!Hv(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function f2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let r=0;r<e.length;++r){let a=e[r],s=t[r];if(a>=0&&s>=0&&a!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=a>=0?a:s}return n}var zne=class{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ge(0),Ut(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return 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r=k("size",e,t,n),a=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),c=k("name",e,t,n),u=new Pne(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ge(1)]}case"TensorArrayWriteV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=k("tensorArrayId",e,t,n),a=k("tensor",e,t,n),s=k("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[ge(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=k("indices",e,t,n),a=k("tensor",e,t,n),s=k("elementShape",e,t,n),i=k("numElements",e,t,n),o=Bne(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=Wne(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=k("tensorListId",e,t,n),a=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=Lne(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=k("tensorListId",e,t,n),a=n.getTensorList(r.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=k("tensorListId",e,t,n),a=k("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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t=wn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(wn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=wn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=al.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Yt(ye(e,"float32"),0),n;n=ze.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return j(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},f6=class{},m6=class extends qt{split(e){return new qre(this,e)}},qre=class extends m6{constructor(e,t){super();this.upstream=e,this.impl=new Xre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xre=class extends y2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Zre=class extends qt{decodeUTF8(){return new Kre(this)}},Kre=class extends m6{constructor(e){super();this.upstream=e,this.impl=new Yre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Yre=class extends y2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=O8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},A6=class extends Zre{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof 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uae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],cae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],hae=[33,133,362,263,1,78,308],nhe=uae.map(e=>W2[e]),rhe=cae.map(e=>W2[e]),ahe=hae.map(e=>W2[e]);var B2=Xr.leftEyeLower0,V2=Xr.rightEyeLower0,ql={leftBounds:[B2[0],B2[B2.length-1]],rightBounds:[V2[0],V2[V2.length-1]]},_0={count:468,mouth:13,symmetryLine:[13,Xr.midwayBetweenEyes[0]]},C6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Xl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function v0(e,t,n,r){for(let a=0;a<L2.length;a++){let{key:s,indices:i}=L2[a],o=Xr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var U2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Vc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?b0(r,[0,0]):w0,l=r!==0?i.map(h=>[...T6(h,o),h[2]]):i,c=r!==0?S6(a):w0,u=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(u,c[0]),h[1]+Ya(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ql.leftBounds[0]][2],r=t[ql.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=x0(g0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Vc(i),l=ze.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&fr.flags.IS_BROWSER&&(l=ze.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Xl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Xl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Xr[`${r}EyeUpper0`][Xl.upperCenter]][2],s=t[Xr[`${r}EyeLower0`][Xl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=k6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=g0(o),c=x0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=_0.count?_0.symmetryLine:C6.symmetryLine;u=P2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,u,0,T);h=b0(-u,N),n.face.mesh.enabled?c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=w0;let _=t.clone();n.face.mesh.enabled?c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=j(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,c,ql.leftBounds[0],ql.leftBounds[1],!0),{box:T,boxSize:C,crop:F}=this.getEyeBox(A,c,ql.rightBounds[0],ql.rightBounds[1]),W=this.irisModel.predict(rt([N,F])).dataSync(),V=W.slice(0,Xl.numCoordinates*3),{rawCoords:U,iris:H}=this.getEyeCoords(V,_,x,!0),X=W.slice(Xl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,C),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(v0(A,U,"left",null),v0(A,G,"right",null)):Y<1?v0(A,U,"left",["EyeUpper0","EyeLower0"]):v0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,H,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=g0(this.calculateLandmarksBoundingBox(y),1.5);let g=kn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=_0.count?_0.symmetryLine:C6.symmetryLine;u=P2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,u,0,T);h=b0(-u,N),c=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},b=x0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var H2=class{constructor(t,n,r,a){this.facePipeline=new U2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Xr))l[h]=Xr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Fi=[null,null,null];async function G2(e){Fi=await Promise.all([!Fi[0]&&e.face.enabled?v6(e):null,!Fi[1]&&e.face.mesh.enabled?ct(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Fi[2]&&e.face.iris.enabled?ct(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new H2(Fi[0],Fi[1],Fi[2],e);return e.face.mesh.enabled&&e.debug&&Ne(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ne(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var dae=Mi;var ng={};Jn(ng,{PoseNet:()=>rg,load:()=>ag});function pae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var q2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=pae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function X2(e){return Math.floor(e/2)}var K2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(X2(t),t);)this.exchange(t,X2(t)),t=X2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function fae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function E6(e,t,n){let[r,a,s]=n.shape,i=new K2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||fae(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var Kl=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Zl=Kl.length,Uc=Kl.reduce((e,t,n)=>(e[t]=n,e),{}),mae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],bhe=mae.map(([e,t])=>[Uc[e],Uc[t]]),R6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Z2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function k0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=Z2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function Y2(e,t,n){return e<t?t:e>n?n:e}function M6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function J2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function F6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function Aae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function yae(e,t){let n=[];for(let r=0;r<Zl;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=Aae(a,s,r,t);n.push(o),n.push(i)}return kn(n,[Zl,2])}function $6(e,t,n){return z(()=>e.toTensor().mul(ge(t,"int32")).toFloat().add(yae(e,n)))}function gae(e,t){return z(()=>{let n=e.div(ge(t,"int32"));return e.sub(n.mul(ge(t,"int32")))})}function D6(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ge(n,"int32")).expandDims(1),o=gae(s,n).expandDims(1);return rt([i,o],1)})}var O6=R6.map(([e,t])=>[Uc[e],Uc[t]]),Q2=O6.map(([,e])=>e),z6=O6.map(([e])=>e),xae=16;function wae(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function eg(e,t,n,r){return{y:Y2(Math.round(e.y/t),0,n-1),x:Y2(Math.round(e.x/t),0,r-1)}}function P6(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=eg(t.position,s,l,c),h=wae(e,u,i),p=J2(t.position,h);for(let A=0;A<o;A++){let y=eg(p,s,l,c),g=Z2(y.y,y.x,n,a);p=J2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=eg(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:Kl[n],score:m}}function L6(e,t,n,r,a,s){let i=t.shape[2],o=Q2.length,l=new Array(i),{part:c,score:u}=e,h=k0(c,r,n);l[c.id]={score:u,part:Kl[c.id],position:h};for(let d=o-1;d>=0;--d){let p=Q2[d],f=z6[d];l[p]&&!l[f]&&(l[f]=P6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=z6[d],f=Q2[d];l[p]&&!l[f]&&(l[f]=P6(d,l[p],f,t,n,r,a))}return l}async function W6(e,t,n){let r=0,a=D6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=$6(l,xae,o),u=await c.buffer(),d=Array.from(F6(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:Kl[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var bae=1,B6=16;function V6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return M6(r,n,i.y,i.x)<=t})}function _ae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(V6(e,t,s,o)||(a+=i),a),0)/n.length}function U6(e,t,n,r,a,s,i){let o=[],l=E6(i,bae,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=k0(u.part,B6,t);if(V6(o,c,h,u.part.id))continue;let d=L6(u,e,t,B6,n,r),p=_ae(o,c,d);p>i&&o.push({keypoints:d,score:p})}return o}async function j6(e){return Promise.all(e.map(t=>t.buffer()))}function 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r=H6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Iae(t,a,n,this.inputSize):await kae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function ag(e){let t=await ct(e.body.modelPath),n=new q2(t);return e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new rg(n)}var ug={};Jn(ug,{HandPose:()=>hg,load:()=>dg});function I0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function jc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function G6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return ze.cropAndResize(t,s,[0],n)}function q6(e,t){let 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g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=j(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let N={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=N0(S0(o),J6),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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t4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],n4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var hr;async function fg(e){return hr||(hr=await ct(e.body.modelPath),hr.width=parseInt(hr.signature.inputs["input_1:0"].tensorShape.dim[2].size),hr.height=parseInt(hr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),hr}async function mg(e,t){if(!hr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=ze.resizeBilinear(e,[hr.width,hr.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let c=await an(()=>hr.predict(a));s=c.result.find(u=>u.size===195||u.size===155).dataSync(),c.result.forEach(u=>u.dispose()),fn("blazepose",c)}else{let c=await hr.predict(a);s=c.find(u=>u.size===195||u.size===155).dataSync(),c.forEach(u=>u.dispose())}a.dispose();let i=[],o=s.length===195?t4:n4,l=5;for(let 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u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ha(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Ln(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},p6=class extends qt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=al.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Yt(ye(e,"float32"),0),n;n=ze.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},m6=class{},A6=class extends qt{split(e){return new Gre(this,e)}},Gre=class extends A6{constructor(e,t){super();this.upstream=e,this.impl=new qre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qre=class extends x2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Kre=class extends qt{decodeUTF8(){return new Xre(this)}},Xre=class extends A6{constructor(e){super();this.upstream=e,this.impl=new Zre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zre=class extends x2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=D8();this.decoder=new 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lae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],uae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],cae=[33,133,362,263,1,78,308],the=lae.map(e=>B2[e]),nhe=uae.map(e=>B2[e]),rhe=cae.map(e=>B2[e]);var V2=Xr.leftEyeLower0,U2=Xr.rightEyeLower0,ql={leftBounds:[V2[0],V2[V2.length-1]],rightBounds:[U2[0],U2[U2.length-1]]},v0={count:468,mouth:13,symmetryLine:[13,Xr.midwayBetweenEyes[0]]},E6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Xl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function k0(e,t,n,r){for(let a=0;a<W2.length;a++){let{key:s,indices:i}=W2[a],o=Xr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var j2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Vc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?_0(r,[0,0]):b0,l=r!==0?i.map(h=>[...C6(h,o),h[2]]):i,u=r!==0?T6(a):b0,c=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(c,u[0]),h[1]+Ya(c,u[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ql.leftBounds[0]][2],r=t[ql.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=w0(x0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Vc(i),l=ze.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&fr.flags.IS_BROWSER&&(l=ze.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Xl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(Xl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Xr[`${r}EyeUpper0`][Xl.upperCenter]][2],s=t[Xr[`${r}EyeLower0`][Xl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=a:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=I6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=x0(o),u=w0(l),c=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:h,landmarks:c}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,u,c=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=v0.count?v0.symmetryLine:E6.symmetryLine;c=L2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,c,0,T);h=_0(-c,N),n.face.mesh.enabled?u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=b0;let _=t.clone();n.face.mesh.enabled?u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:u};let[,d,p]=this.meshDetector.predict(u),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,u,ql.leftBounds[0],ql.leftBounds[1],!0),{box:T,boxSize:C,crop:F}=this.getEyeBox(A,u,ql.rightBounds[0],ql.rightBounds[1]),W=this.irisModel.predict(rt([N,F])).dataSync(),V=W.slice(0,Xl.numCoordinates*3),{rawCoords:U,iris:j}=this.getEyeCoords(V,_,x,!0),X=W.slice(Xl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,C),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(k0(A,U,"left",null),k0(A,G,"right",null)):Y<1?k0(A,U,"left",["EyeUpper0","EyeLower0"]):k0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,j,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,c,h);i=x0(this.calculateLandmarksBoundingBox(y),1.5);let g=kn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&fr.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=v0.count?v0.symmetryLine:E6.symmetryLine;c=L2(i.landmarks[_],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],C=ze.rotateWithOffset(t,c,0,T);h=_0(-c,N),u=Gl({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:u,rawCoords:A},b=w0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var G2=class{constructor(t,n,r,a){this.facePipeline=new j2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Xr))l[h]=Xr[h].map(d=>i[d]);let u=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[2],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,c=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:u,boxRaw:c,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Fi=[null,null,null];async function q2(e){Fi=await Promise.all([!Fi[0]&&e.face.enabled?k6(e):null,!Fi[1]&&e.face.mesh.enabled?ct(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Fi[2]&&e.face.iris.enabled?ct(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new G2(Fi[0],Fi[1],Fi[2],e);return e.face.mesh.enabled&&e.debug&&Ne(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ne(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var hae=Mi;var rg={};Jn(rg,{PoseNet:()=>ag,load:()=>sg});function dae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var X2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=dae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function K2(e){return Math.floor(e/2)}var Z2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(K2(t),t);)this.exchange(t,K2(t)),t=K2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function pae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,u=Math.max(n-a,0),c=Math.min(n+a+1,i);for(let h=u;h<c;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function R6(e,t,n){let[r,a,s]=n.shape,i=new 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Kl=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Zl=Kl.length,Uc=Kl.reduce((e,t,n)=>(e[t]=n,e),{}),fae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],whe=fae.map(([e,t])=>[Uc[e],Uc[t]]),M6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Y2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function I0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=Y2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function J2(e,t,n){return e<t?t:e>n?n:e}function F6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function Q2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function $6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function mae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+Zl)}}function Aae(e,t){let n=[];for(let r=0;r<Zl;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=mae(a,s,r,t);n.push(o),n.push(i)}return kn(n,[Zl,2])}function D6(e,t,n){return z(()=>e.toTensor().mul(ge(t,"int32")).toFloat().add(Aae(e,n)))}function yae(e,t){return z(()=>{let n=e.div(ge(t,"int32"));return e.sub(n.mul(ge(t,"int32")))})}function O6(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ge(n,"int32")).expandDims(1),o=yae(s,n).expandDims(1);return rt([i,o],1)})}var z6=M6.map(([e,t])=>[Uc[e],Uc[t]]),eg=z6.map(([,e])=>e),P6=z6.map(([e])=>e),gae=16;function xae(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function tg(e,t,n,r){return{y:J2(Math.round(e.y/t),0,n-1),x:J2(Math.round(e.x/t),0,r-1)}}function L6(e,t,n,r,a,s,i,o=2){let[l,u]=r.shape,c=tg(t.position,s,l,u),h=xae(e,c,i),p=Q2(t.position,h);for(let A=0;A<o;A++){let y=tg(p,s,l,u),g=Y2(y.y,y.x,n,a);p=Q2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=tg(p,s,l,u),m=r.get(f.y,f.x,n);return{position:p,part:Kl[n],score:m}}function W6(e,t,n,r,a,s){let i=t.shape[2],o=eg.length,l=new Array(i),{part:u,score:c}=e,h=I0(u,r,n);l[u.id]={score:c,part:Kl[u.id],position:h};for(let d=o-1;d>=0;--d){let p=eg[d],f=P6[d];l[p]&&!l[f]&&(l[f]=L6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=P6[d],f=eg[d];l[p]&&!l[f]&&(l[f]=L6(d,l[p],f,t,n,r,a))}return l}async function B6(e,t,n){let r=0,a=O6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],u=D6(l,gae,o),c=await u.buffer(),d=Array.from($6(i,l)).map((f,m)=>(r+=f,{position:{y:c.get(m,0),x:c.get(m,1)},part:Kl[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),u.dispose(),{keypoints:p,score:r/d.length}}var wae=1,V6=16;function U6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return F6(r,n,i.y,i.x)<=t})}function bae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(U6(e,t,s,o)||(a+=i),a),0)/n.length}function j6(e,t,n,r,a,s,i){let o=[],l=R6(i,wae,e),u=a^2;for(;o.length<s&&!l.empty();){let c=l.dequeue(),h=I0(c.part,V6,t);if(U6(o,u,h,c.part.id))continue;let d=W6(c,e,t,V6,n,r),p=bae(o,u,d);p>i&&o.push({keypoints:d,score:p})}return o}async function H6(e){return Promise.all(e.map(t=>t.buffer()))}function 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r=G6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await kae(t,a,n,this.inputSize):await vae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function sg(e){let t=await ct(e.body.modelPath),n=new X2(t);return e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new ag(n)}var cg={};Jn(cg,{HandPose:()=>dg,load:()=>pg});function N0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function jc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function q6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return ze.cropAndResize(t,s,[0],n)}function X6(e,t){let 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g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let w=H(y,[-1,3]),b=w.arraySync();y.dispose(),w.dispose();let _=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(_);this.storedBoxes[i]=x;let N={landmarks:_,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=S0(T0(o),Q6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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n4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],r4=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var hr;async function mg(e){return hr||(hr=await ct(e.body.modelPath),hr.width=parseInt(hr.signature.inputs["input_1:0"].tensorShape.dim[2].size),hr.height=parseInt(hr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ne(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),hr}async function Ag(e,t){if(!hr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=ze.resizeBilinear(e,[hr.width,hr.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>hr.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),fn("blazepose",u)}else{let u=await hr.predict(a);s=u.find(c=>c.size===195||c.size===155).dataSync(),u.forEach(c=>c.dispose())}a.dispose();let i=[],o=s.length===195?n4:r4,l=5;for(let 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a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&kg(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),$0(n,s)}}}}async function p4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ie.font;for(let r of t){if(ie.drawBoxes&&(n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]),ie.drawLabels&&(ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+ie.lineHeight,r.box[2])),n.fillStyle=ie.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+ie.lineHeight,r.box[2])),n.stroke()),ie.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=ie.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:ie.color,F0(n,a[0],a[1]);if(ie.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=ie.lineWidth,n.beginPath(),n.strokeStyle=ie.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:ie.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function f4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ie.font;for(let r of t)if(ie.drawBoxes){if(n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]),ie.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+ie.lineHeight,r.box[2])),n.fillStyle=ie.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+ie.lineHeight,r.box[2])}n.stroke()}}}async function Dae(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Oae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(h4(e,t.face),d4(e,t.body),p4(e,t.hand),c4(e,t.gesture),f4(e,t.object))}var D0=`
+`)}var $0=2048,Tt=null,tn=null,Rt=null;function vg(e,t){let n;if(e instanceof Ve)n=Rr(e);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>$0&&(i=$0,o=i*s/a),o>$0&&(o=$0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)return Ne("Human: invalid input",e),{tensor:null,canvas:null};(!Tt||Tt.width!==i||Tt.height!==o)&&(Tt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Tt.width!==i&&(Tt.width=i),Tt.height!==o&&(Tt.height=o));let l=Tt.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Tt.width,Tt.height),t.filter.enabled){if((!Rt||!tn||Tt.width!==tn.width||Tt.height!==tn.height)&&(tn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Tt.width,Tt.height):document.createElement("canvas"),tn.width!==Tt.width&&(tn.width=Tt.width),tn.height!==Tt.height&&(tn.height=Tt.height),Rt=fr.flags.IS_BROWSER?new l4({canvas:tn}):null),!Rt)return{tensor:null,canvas:Tt};Rt.reset(),Rt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Rt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Rt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Rt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Rt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Rt.addFilter("hue",t.filter.hue),t.filter.negative&&Rt.addFilter("negative"),t.filter.sepia&&Rt.addFilter("sepia"),t.filter.vintage&&Rt.addFilter("brownie"),t.filter.sepia&&Rt.addFilter("sepia"),t.filter.kodachrome&&Rt.addFilter("kodachrome"),t.filter.technicolor&&Rt.addFilter("technicolor"),t.filter.polaroid&&Rt.addFilter("polaroid"),t.filter.pixelate!==0&&Rt.addFilter("pixelate",t.filter.pixelate),Rt.apply(Tt)}else tn=Tt,Rt&&(Rt=null);let u;if(tn.data){let h=[tn.height,tn.width,3];u=ld(tn.data,h,"int32")}else if(t.backend==="webgl"||tn instanceof ImageData)u=al.fromPixels(tn);else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(tn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);u=al.fromPixels(p)}let c=u.toFloat();n=c.expandDims(0),u.dispose(),c.dispose()}let r=t.filter.return?tn:null;return{tensor:n,canvas:r}}var kg={};Jn(kg,{all:()=>Dae,body:()=>h4,canvas:()=>$ae,drawOptions:()=>ie,face:()=>c4,gesture:()=>u4,hand:()=>d4,object:()=>p4});var pt={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var ie={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function D0(e,t,n,r=null){e.fillStyle=ie.useDepth&&r?`rgba(${127.5+2*(r||0)}, ${127.5-2*(r||0)}, 255, 0.3)`:ie.color,e.beginPath(),e.arc(t,n,ie.pointSize,0,2*Math.PI),e.fill()}function Yl(e,t,n,r,a){if(e.beginPath(),ie.useCurves){let s=(t+t+r)/2,i=(n+n+a)/2;e.ellipse(s,i,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=ie.lineWidth,e.moveTo(t+ie.roundRect,n),e.lineTo(t+r-ie.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+ie.roundRect),e.lineTo(t+r,n+a-ie.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-ie.roundRect,n+a),e.lineTo(t+ie.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-ie.roundRect),e.lineTo(t,n+ie.roundRect),e.quadraticCurveTo(t,n,t+ie.roundRect,n),e.closePath();e.stroke()}function Ig(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=ie.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:ie.color,e.fillStyle=ie.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:ie.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),ie.fillPolygons&&(e.closePath(),e.fill())}}function O0(e,t=[]){if(!(t===void 0||t.length===0)){if(!ie.useCurves||t.length<=2){Ig(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,a=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),ie.fillPolygons&&(e.closePath(),e.fill())}}async function u4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=ie.font,n.fillStyle=ie.color;let r=1;for(let a=0;a<t.length;a++){let s=[],i=[];if([s,i]=Object.entries(t[a]),i.length>1&&i[1].length>0){let o=s[1]>0?`#${s[1]}`:"",l=`${s[0]} ${o}: ${i[1]}`;ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(l,8,2+r*ie.lineHeight)),n.fillStyle=ie.labelColor,n.fillText(l,6,0+r*ie.lineHeight),r+=1}}}async function c4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let r of t){n.font=ie.font,n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.drawBoxes&&(ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]));let a=[];if(a.push(`face confidence: ${Math.trunc(100*r.confidence)}%`),r.genderConfidence&&a.push(`${r.gender||""} ${Math.trunc(100*r.genderConfidence)}% confident`),r.age&&a.push(`age: ${r.age||""}`),r.iris&&a.push(`iris distance: ${r.iris}`),r.emotion&&r.emotion.length>0){let s=r.emotion.map(i=>`${Math.trunc(100*i.score)}% ${i.emotion}`);a.push(s.join(" "))}r.angle&&r.angle.roll&&a.push(`roll: ${Math.trunc(100*r.angle.roll)/100} yaw:${Math.trunc(100*r.angle.yaw)/100} pitch:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=ie.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*ie.lineHeight+r.box[1];ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=ie.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh&&r.mesh.length>0){if(ie.drawPoints)for(let s of r.mesh)D0(n,s[0],s[1],s[2]);if(ie.drawPolygons){n.lineWidth=1;for(let s=0;s<Mi.length/3;s++){let i=[Mi[s*3+0],Mi[s*3+1],Mi[s*3+2]].map(o=>r.mesh[o]);Ig(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=ie.useDepth?"rgba(255, 200, 255, 0.3)":ie.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ie.fillPolygons&&(n.fillStyle=ie.useDepth?"rgba(255, 255, 200, 0.3)":ie.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=ie.useDepth?"rgba(255, 200, 255, 0.3)":ie.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ie.fillPolygons&&(n.fillStyle=ie.useDepth?"rgba(255, 255, 200, 0.3)":ie.color,n.fill())}}}}}var Qa=[];async function h4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!Qa[r]&&ie.bufferedOutput&&(Qa[r]={...t[r]}),n.strokeStyle=ie.color,n.lineWidth=ie.lineWidth,ie.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=ie.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:ie.color,ie.bufferedOutput?(Qa[r].keypoints[a][0]=(Qa[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,Qa[r].keypoints[a][1]=(Qa[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,D0(n,Qa[r].keypoints[a][0],Qa[r].keypoints[a][1])):D0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(ie.drawLabels&&(n.font=ie.font,t[r].keypoints))for(let a of t[r].keypoints)n.fillStyle=ie.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:ie.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4);if(ie.drawPolygons&&t[r].keypoints){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&Ig(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>pt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),O0(n,s)}}}}async 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0.5)`:ie.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function p4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ie.font;for(let r of t)if(ie.drawBoxes){if(n.strokeStyle=ie.color,n.fillStyle=ie.color,ie.useRawBoxes?Yl(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):Yl(n,r.box[0],r.box[1],r.box[2],r.box[3]),ie.drawLabels){let a=`${Math.round(100*r.score)}% ${r.label}`;ie.shadowColor&&ie.shadowColor!==""&&(n.fillStyle=ie.shadowColor,n.fillText(a,r.box[0]+3,1+r.box[1]+ie.lineHeight,r.box[2])),n.fillStyle=ie.labelColor,n.fillText(a,r.box[0]+2,0+r.box[1]+ie.lineHeight,r.box[2])}n.stroke()}}}async function $ae(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Dae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(c4(e,t.face),h4(e,t.body),d4(e,t.hand),u4(e,t.gesture),p4(e,t.object))}var z0=`
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backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ne("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ne(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ne("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&g6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ne("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ne("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ne(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-n)}});P0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(D0);break;case"full":n=await t(O0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});L0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+D0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+O0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));W0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(D0):t(O0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=vg,this.version=m4,this.config=qi(pt,t),this.state="idle",rs(this,Jl,0),rs(this,Hc,!1),rs(this,Gc,!1),rs(this,$i,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>_g(n,this.config),this.classes={facemesh:j2,age:w2,gender:_2,emotion:S2,faceres:F2,body:this.config.body.modelPath.includes("posenet")?ng:pg,hand:ug,nanodet:gg},this.sysinfo=Pg()}profileData(){return this.config.profile?x2:{}}similarity(t,n){return this.config.face.description.enabled?D2(t,n):this.config.face.embedding.enabled?x6(t,n):0}enhance(t){return O2(t)}match(t,n,r=0){return w6(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=qi(this.config,t)),Qn(this,$i)&&(this.config.debug&&Ne(`version: 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Promise.all([this.models.face||(this.config.face.enabled?G2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?b2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?N2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?E2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?R2(this.config):null),this.models.handpose||(this.config.hand.enabled?dg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?ag(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?fg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Ag(this.config):null),this.models.nanodet||(this.config.object.enabled?wg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?$2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await G2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await b2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await N2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await E2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await R2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await dg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await ag(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await fg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Ag(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await wg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await $2(this.config))),Qn(this,$i)&&(this.config.debug&&Ne("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),rs(this,$i,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var m,A,y,g;this.state="config";let a;this.config=qi(this.config,n),this.state="check";let s=Qn(this,z0).call(this,t);s&&(Ne(s,t),r({error:s}));let i=Ye();await Qn(this,qc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=Ye();let o=_g(t,this.config);if(!o||!o.tensor){Ne("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let l,c,u,h,d;this.config.async?(u=this.config.face.enabled?z2(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),u=this.config.face.enabled?await z2(this,o.tensor):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?mg(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?yg(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await mg(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?await yg(o.tensor,this.config):[]),d=Math.trunc(Ye()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(c=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),c=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?bg(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),h=this.config.object.enabled?await bg(o.tensor,this.config):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([u,l,c,h]=await Promise.all([u,l,c,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(a=Ye(),p=[...i4(u),...s4(l),...l4(c),...o4(u)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let f={face:u,body:l,hand:c,gesture:p,object:h,performance:this.perf,canvas:o.canvas};r(f)})}async warmup(t={}){let n=Ye();t&&(this.config=qi(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await Qn(this,P0).call(this):typeof Image!="undefined"?a=await Qn(this,L0).call(this):a=await Qn(this,W0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&Ne("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};Jl=new WeakMap,Hc=new WeakMap,Gc=new WeakMap,$i=new WeakMap,z0=new WeakMap,qc=new WeakMap,P0=new WeakMap,L0=new WeakMap,W0=new WeakMap;return zae;})();
+2Q==`;var f4="1.2.5";var Jl,Hc,Gc,$i,L0,qc,W0,B0,V0,m4=class{constructor(t={}){Jl.set(this,void 0);Hc.set(this,void 0);Gc.set(this,void 0);$i.set(this,void 0);this.analyze=(...t)=>{if(!Qn(this,Hc))return;let n=this.tf.engine().state.numTensors,r=Qn(this,Jl);rs(this,Jl,n);let a=n-r;a!==0&&Ne(...t,a)};L0.set(this,t=>{if(!Qn(this,Gc))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Ve))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});qc.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=Ye();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&Ne("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ne("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ne(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ne("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&x6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ne("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ne("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ne(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-n)}});W0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(z0);break;case"full":n=await t(P0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});B0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+z0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+P0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));V0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(z0):t(P0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=kg,this.version=f4,this.config=qi(pt,t),this.state="idle",rs(this,Jl,0),rs(this,Hc,!1),rs(this,Gc,!1),rs(this,$i,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>vg(n,this.config),this.classes={facemesh:H2,age:b2,gender:v2,emotion:T2,faceres:$2,body:this.config.body.modelPath.includes("posenet")?rg:fg,hand:cg,nanodet:xg},this.sysinfo=Lg()}profileData(){return this.config.profile?l0:{}}similarity(t,n){return this.config.face.description.enabled?O2(t,n):this.config.face.embedding.enabled?w6(t,n):0}enhance(t){return z2(t)}match(t,n,r=0){return b6(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=qi(this.config,t)),Qn(this,$i)&&(this.config.debug&&Ne(`version: ${this.version}`),this.config.debug&&Ne(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ne("platform:",this.sysinfo.platform),this.config.debug&&Ne("agent:",this.sysinfo.agent),await Qn(this,qc).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ne("configuration:",this.config),this.config.debug&&Ne("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?q2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?_2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?S2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?R2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?M2(this.config):null),this.models.handpose||(this.config.hand.enabled?pg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?sg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?mg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?yg(this.config):null),this.models.nanodet||(this.config.object.enabled?bg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?D2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await q2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await _2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await S2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await R2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await M2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await pg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await sg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await mg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await yg(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await bg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await D2(this.config))),Qn(this,$i)&&(this.config.debug&&Ne("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),rs(this,$i,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var m,A,y,g;this.state="config";let a;this.config=qi(this.config,n),this.state="check";let s=Qn(this,L0).call(this,t);s&&(Ne(s,t),r({error:s}));let i=Ye();await Qn(this,qc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=Ye();let o=vg(t,this.config);if(!o||!o.tensor){Ne("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let l,u,c,h,d;this.config.async?(c=this.config.face.enabled?P2(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),c=this.config.face.enabled?await P2(this,o.tensor):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?Ag(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?gg(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await Ag(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(l=this.config.body.enabled?await gg(o.tensor,this.config):[]),d=Math.trunc(Ye()-a),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(y=this.models.handpose)==null?void 0:y.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?_g(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),h=this.config.object.enabled?await _g(o.tensor,this.config):[],d=Math.trunc(Ye()-a),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([c,l,u,h]=await Promise.all([c,l,u,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(a=Ye(),p=[...s4(c),...a4(l),...o4(u),...i4(c)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let f={face:c,body:l,hand:u,gesture:p,object:h,performance:this.perf,canvas:o.canvas};r(f)})}async warmup(t={}){let n=Ye();t&&(this.config=qi(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await Qn(this,W0).call(this):typeof Image!="undefined"?a=await Qn(this,B0).call(this):a=await Qn(this,V0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&Ne("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};Jl=new WeakMap,Hc=new WeakMap,Gc=new WeakMap,$i=new WeakMap,L0=new WeakMap,qc=new WeakMap,W0=new WeakMap,B0=new WeakMap,V0=new WeakMap;return Oae;})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.js.map b/dist/human.js.map
index 717bdc1a..d580fa10 100644
--- a/dist/human.js.map
+++ b/dist/human.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/human.ts", "../src/helpers.ts", "../src/sysinfo.ts", "../node_modules/.pnpm/node-fetch@2.6.1/node_modules/node-fetch/browser.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js", "(disabled):path", "(disabled):worker_threads", "(disabled):perf_hooks", "../node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js", "../node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js", "../node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/profiler.ts", 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"../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", 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"../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/imagefx.js", "../src/image/image.ts", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts"],
-  "sourcesContent": ["import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      tf.enableDebugMode();\n      */\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var 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msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var 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_sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 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asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=function(){return(___pthread_tsd_run_dtors=Module[\"___pthread_tsd_run_dtors\"]=Module[\"asm\"][\"db\"]).apply(null,arguments)};var _emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=function(){return(_emscripten_main_thread_process_queued_calls=Module[\"_emscripten_main_thread_process_queued_calls\"]=Module[\"asm\"][\"eb\"]).apply(null,arguments)};var _emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=function(){return(_emscripten_current_thread_process_queued_calls=Module[\"_emscripten_current_thread_process_queued_calls\"]=Module[\"asm\"][\"fb\"]).apply(null,arguments)};var _emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n  return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var 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doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(name: string, raw: any): void {\n  if (!raw || !raw.kernels) return;\n  const maxResults = 5;\n  const time = raw.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n  data[name] = res;\n  log('Human profiler', name, res);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    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y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\nconst activateScore = false;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position\n          if (score > config.object.minConfidence) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            let boxRaw = [ // results normalized to range 0..1\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n              cx + (scaleBox / strideSize * boxOffset[2]),\n              cy + (scaleBox / strideSize * boxOffset[3]),\n            ];\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n  const nmsIdx = nms.dataSync();\n  tf.dispose(nms);\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    // @ts-ignore\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    resize.dispose();\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result.clone();\n      profileObject.result.dispose();\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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+  "sourcesContent": ["import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      this.tf.enableDebugMode();\n      */\n      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 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xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var 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print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var 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_RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof 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parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs: packageBundle.version,\n  'tfjs-core': packageCore.version,\n  'tfjs-data': packageData.version,\n  'tfjs-layers': packageLayers.version,\n  'tfjs-converter': packageConverter.version,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.executeAsync(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index 30b58753..d07ee323 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -5,14 +5,14 @@
   author: <https://github.com/vladmandic>'
   */
 
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hn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],an=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],xn=[33,133,362,263,1,78,308],lr=hn.map(e=>I1[e]),dr=an.map(e=>I1[e]),fr=xn.map(e=>I1[e]);var Z1=ce.leftEyeLower0,L1=ce.rightEyeLower0,ue={leftBounds:[Z1[0],Z1[Z1.length-1]],rightBounds:[L1[0],L1[L1.length-1]]},Je={count:468,mouth:13,symmetryLine:[13,ce.midwayBetweenEyes[0]]},Ct={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ke(e,t,n,c){for(let r=0;r<O1.length;r++){let{key:A,indices:_}=O1[r],o=ce[`${n}${A}`];if(!c||c.includes(A))for(let s=0;s<_.length;s++){let i=_[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var V1=class{constructor(t,n,c){var r,A;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=c,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((A=t==null?void 0:t.model)==null?void 0:A.inputs[0].shape[2]),this.irisSize=(c==null?void 0:c.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,c,r){let A=ze({startPoint:n.startPoint,endPoint:n.endPoint}),_=t.map(h=>[A[0]/this.meshSize*(h[0]-this.meshSize/2),A[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=c!==0?Ye(c,[0,0]):Ce,s=c!==0?_.map(h=>[...Bt(h,o),h[2]]):_,i=c!==0?qt(r):Ce,a=[...pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+he(a,i[0]),h[1]+he(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ue.leftBounds[0]][2],c=t[ue.rightBounds[0]][2];return n-c}getEyeBox(t,n,c,r,A=!1){let _=Be(qe(this.calculateLandmarksBoundingBox([t[c],t[r]]),this.irisEnlarge)),o=ze(_),s=X.image.cropAndResize(n,[[_.startPoint[1]/this.meshSize,_.startPoint[0]/this.meshSize,_.endPoint[1]/this.meshSize,_.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return A&&X.ENV.flags.IS_BROWSER&&(s=X.image.flipLeftRight(s)),{box:_,boxSize:o,crop:s}}getEyeCoords(t,n,c,r=!1){let A=[];for(let _=0;_<be.numCoordinates;_++){let o=t[_*3],s=t[_*3+1],i=t[_*3+2];A.push([(r?1-o/this.irisSize:o/this.irisSize)*c[0]+n.startPoint[0],s/this.irisSize*c[1]+n.startPoint[1],i])}return{rawCoords:A,iris:A.slice(be.index)}}getAdjustedIrisCoords(t,n,c){let r=t[ce[`${c}EyeUpper0`][be.upperCenter]][2],A=t[ce[`${c}EyeLower0`][be.lowerCenter]][2],_=(r+A)/2;return n.map((o,s)=>{let i=_;return s===2?i=r:s===4&&(i=A),[o[0],o[1],i]})}async predict(t,n){let c=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let _ of r.boxes)this.storedBoxes.push({startPoint:_.box.startPoint.dataSync(),endPoint:_.box.endPoint.dataSync(),landmarks:_.landmarks,confidence:_.confidence});this.storedBoxes.length>0&&(c=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),c){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let _=0;_<this.storedBoxes.length;_++){let o=Ht({startPoint:this.storedBoxes[_].startPoint,endPoint:this.storedBoxes[_].endPoint},r.scaleFactor),s=qe(o),i=Be(s),a=this.storedBoxes[_].landmarks.arraySync(),h=this.storedBoxes[_].confidence;this.storedBoxes[_]={...i,confidence:h,landmarks:a}}}r&&r.boxes&&r.boxes.forEach(_=>{_.box.startPoint.dispose(),_.box.endPoint.dispose(),_.landmarks.dispose()});let A=X.tidy(()=>this.storedBoxes.map((_,o)=>{let s=_.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=Je.count?Je.symmetryLine:Ct.symmetryLine;a=k1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),P=[p[0]/t.shape[2],p[1]/t.shape[1]],M=X.image.rotateWithOffset(t,a,0,P);h=Ye(-a,p),n.face.mesh.enabled?i=me({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.meshSize,this.meshSize]).div(255):i=me({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.boxSize,this.boxSize]).div(255)}else{h=Ce;let m=t.clone();n.face.mesh.enabled?i=me({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.meshSize,this.meshSize]).div(255):i=me({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,boxConfidence:s,confidence:_.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),f=y.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let z=X.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:m,boxSize:l,crop:p}=this.getEyeBox(z,i,ue.leftBounds[0],ue.leftBounds[1],!0),{box:P,boxSize:M,crop:C}=this.getEyeBox(z,i,ue.rightBounds[0],ue.rightBounds[1]),_e=this.irisModel.predict(X.concat([p,C])).dataSync(),I0=_e.slice(0,be.numCoordinates*3),{rawCoords:vt,iris:Z0}=this.getEyeCoords(I0,m,l,!0),L0=_e.slice(be.numCoordinates*3),{rawCoords:zt,iris:V0}=this.getEyeCoords(L0,P,M),Mt=this.getLeftToRightEyeDepthDifference(z);Math.abs(Mt)<30?(Ke(z,vt,"left",null),Ke(z,zt,"right",null)):Mt<1?Ke(z,vt,"left",["EyeUpper0","EyeLower0"]):Ke(z,zt,"right",["EyeUpper0","EyeLower0"]);let H0=this.getAdjustedIrisCoords(z,Z0,"left"),X0=this.getAdjustedIrisCoords(z,V0,"right");z=z.concat(H0).concat(X0)}let b=this.transformRawCoords(z,_,a,h);_=qe(this.calculateLandmarksBoundingBox(b),1.5);let W=X.tensor2d(b);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=Je.count?Je.symmetryLine:Ct.symmetryLine;a=k1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),P=[p[0]/t.shape[2],p[1]/t.shape[1]],M=X.image.rotateWithOffset(t,a,0,P);h=Ye(-a,p),i=me({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.meshSize,this.meshSize]).div(255)}let E={coords:W,box:_,faceConfidence:f,boxConfidence:s,image:i,rawCoords:z},F=Be(_);return this.storedBoxes[o]={...F,landmarks:b,confidence:_.confidence,faceConfidence:f},E}));return A=A.filter(_=>_!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(_=>_.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=A.length,A}calculateLandmarksBoundingBox(t){let n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A,landmarks:t}}};var F1=class{constructor(t,n,c,r){this.facePipeline=new V1(t,n,c),this.config=r}async estimateFaces(t,n){let c=await this.facePipeline.predict(t,n),r=[];for(let A of c||[]){if(A.isDisposedInternal)continue;let _=A.coords?A.coords.arraySync():[],o=_.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(_&&_.length>0)for(let h of Object.keys(ce))s[h]=ce[h].map(y=>_[y]);let i=A.box?[Math.max(0,A.box.startPoint[0]),Math.max(0,A.box.startPoint[1]),Math.min(t.shape[1],A.box.endPoint[0])-Math.max(0,A.box.startPoint[0]),Math.min(t.shape[2],A.box.endPoint[1])-Math.max(0,A.box.startPoint[1])]:0,a=A.box?[A.box.startPoint[0]/t.shape[2],A.box.startPoint[1]/t.shape[1],(A.box.endPoint[0]-A.box.startPoint[0])/t.shape[2],(A.box.endPoint[1]-A.box.startPoint[1])/t.shape[1]]:[];r.push({confidence:A.faceConfidence||A.boxConfidence||0,boxConfidence:A.boxConfidence,faceConfidence:A.faceConfidence,box:i,boxRaw:a,mesh:_,meshRaw:o,annotations:s,image:A.image?A.image.clone():null}),A.coords&&A.coords.dispose(),A.image&&A.image.dispose()}return r}},le=[null,null,null];async function q1(e){le=await Promise.all([!le[0]&&e.face.enabled?Lt(e):null,!le[1]&&e.face.mesh.enabled?X1.loadGraphModel(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!le[2]&&e.face.iris.enabled?X1.loadGraphModel(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new F1(le[0],le[1],le[2],e);return e.face.mesh.enabled&&e.debug&&u(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&u(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var yn=we;var $1={};Q($1,{PoseNet:()=>et,load:()=>tt});var h0=k(N());var Yt=k(N());function wn(e){let[t,n,c,r]=e;return{offsets:t,heatmap:n,displacementFwd:c,displacementBwd:r}}var B1=class{constructor(t){this.model=t}predict(t){return Yt.tidy(()=>{let c=t.toFloat().div(127.5).sub(1).expandDims(0),A=this.model.predict(c).map(o=>o.squeeze([0])),_=wn(A);return{heatmapScores:_.heatmap.sigmoid(),offsets:_.offsets,displacementFwd:_.displacementFwd,displacementBwd:_.displacementBwd}})}dispose(){this.model.dispose()}};function C1(e){return Math.floor(e/2)}var Y1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(C1(t),t);)this.exchange(t,C1(t)),t=C1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let c=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=c}};function ln(e,t,n,c,r,A){let[_,o]=A.shape,s=!0,i=Math.max(n-r,0),a=Math.min(n+r+1,_);for(let h=i;h<a;++h){let y=Math.max(c-r,0),d=Math.min(c+r+1,o);for(let f=y;f<d;++f)if(A.get(h,f,e)>t){s=!1;break}if(!s)break}return s}function Jt(e,t,n){let[c,r,A]=n.shape,_=new Y1(c*r*A,({score:o})=>o);for(let o=0;o<c;++o)for(let s=0;s<r;++s)for(let i=0;i<A;++i){let a=n.get(o,s,i);a<e||ln(i,a,o,s,t,n)&&_.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return _}var ge=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Te=ge.length,Me=ge.reduce((e,t,n)=>(e[t]=n,e),{}),dn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],jr=dn.map(([e,t])=>[Me[e],Me[t]]),Kt=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function J1(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Te)}}function Ue(e,t,n){let{heatmapY:c,heatmapX:r,id:A}=e,{y:_,x:o}=J1(c,r,A,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+_}}function K1(e,t,n){return e<t?t:e>n?n:e}function Ut(e,t,n,c){let r=n-e,A=c-t;return r*r+A*A}function U1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var J=k(N());function Dt(e,t){let n=t.shape[0],c=new Float32Array(n);for(let r=0;r<n;r++){let A=t.get(r,0),_=t.get(r,1);c[r]=e.get(A,_,r)}return c}function fn(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Te)}}function pn(e,t){let n=[];for(let c=0;c<Te;c++){let r=e.get(c,0).valueOf(),A=e.get(c,1).valueOf(),{x:_,y:o}=fn(r,A,c,t);n.push(o),n.push(_)}return J.tensor2d(n,[Te,2])}function Gt(e,t,n){return J.tidy(()=>e.toTensor().mul(J.scalar(t,"int32")).toFloat().add(pn(e,n)))}function mn(e,t){return J.tidy(()=>{let n=e.div(J.scalar(t,"int32"));return e.sub(n.mul(J.scalar(t,"int32")))})}function Qt(e){let[t,n,c]=e.shape;return J.tidy(()=>{let A=e.reshape([t*n,c]).argMax(0),_=A.div(J.scalar(n,"int32")).expandDims(1),o=mn(A,n).expandDims(1);return J.concat([_,o],1)})}var $t=Kt.map(([e,t])=>[Me[e],Me[t]]),D1=$t.map(([,e])=>e),e0=$t.map(([e])=>e),un=16;function bn(e,t,n){let c=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,c+e)}}function G1(e,t,n,c){return{y:K1(Math.round(e.y/t),0,n-1),x:K1(Math.round(e.x/t),0,c-1)}}function t0(e,t,n,c,r,A,_,o=2){let[s,i]=c.shape,a=G1(t.position,A,s,i),h=bn(e,a,_),d=U1(t.position,h);for(let z=0;z<o;z++){let b=G1(d,A,s,i),W=J1(b.y,b.x,n,r);d=U1({x:b.x*A,y:b.y*A},{x:W.x,y:W.y})}let f=G1(d,A,s,i),x=c.get(f.y,f.x,n);return{position:d,part:ge[n],score:x}}function n0(e,t,n,c,r,A){let _=t.shape[2],o=D1.length,s=new Array(_),{part:i,score:a}=e,h=Ue(i,c,n);s[i.id]={score:a,part:ge[i.id],position:h};for(let y=o-1;y>=0;--y){let d=D1[y],f=e0[y];s[d]&&!s[f]&&(s[f]=t0(y,s[d],f,t,n,c,A))}for(let y=0;y<o;++y){let d=e0[y],f=D1[y];s[d]&&!s[f]&&(s[f]=t0(y,s[d],f,t,n,c,r))}return s}async function r0(e,t,n){let c=0,r=Qt(e),A=await Promise.all([e.buffer(),t.buffer(),r.buffer()]),_=A[0],o=A[1],s=A[2],i=Gt(s,un,o),a=await i.buffer(),y=Array.from(Dt(_,s)).map((f,x)=>(c+=f,{position:{y:a.get(x,0),x:a.get(x,1)},part:ge[x],score:f})),d=y.filter(f=>f.score>n);return r.dispose(),i.dispose(),{keypoints:d,score:c/y.length}}var gn=1,c0=16;function A0(e,t,{x:n,y:c},r){return e.some(({keypoints:A})=>{let _=A[r].position;return Ut(c,n,_.y,_.x)<=t})}function Tn(e,t,n){return n.reduce((r,{position:A,score:_},o)=>(A0(e,t,A,o)||(r+=_),r),0)/n.length}function _0(e,t,n,c,r,A,_){let o=[],s=Jt(_,gn,e),i=r^2;for(;o.length<A&&!s.empty();){let a=s.dequeue(),h=Ue(a.part,c0,t);if(A0(o,i,h,a.part.id))continue;let y=n0(a,e,t,c0,n,c),d=Tn(o,i,y);d>_&&o.push({keypoints:y,score:d})}return o}async function o0(e){return Promise.all(e.map(t=>t.buffer()))}function Pn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:c,part:r,position:A})=>({score:c,part:r,position:{x:Math.trunc(A.x*n),y:Math.trunc(A.y*t)}}))}}function s0(e,[t,n]){let c=e.squeeze(0),r=c.resizeBilinear([t,n]);return c.dispose(),r}function Q1(e,[t,n],[c,r]){return e.map(_=>Pn(_,t/c,n/r))}async function vn(e,t,n,c){return new Promise(async r=>{let A=await o0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),_=A[0],o=A[1],s=A[2],i=A[3],a=await _0(_,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=Q1(a,[e.shape[1],e.shape[2]],[c,c]);r(h)})}async function zn(e,t,n,c){return new Promise(async r=>{let A=await r0(t.heatmapScores,t.offsets,n.body.scoreThreshold),_=Q1([A],[e.shape[1],e.shape[2]],[c,c]);r(_)})}var et=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let c=s0(t,[this.inputSize,this.inputSize]),r=this.baseModel.predict(c,n),A=n.body.maxDetections<2?await zn(t,r,n,this.inputSize):await vn(t,r,n,this.inputSize);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),c.dispose(),A}dispose(){this.baseModel.dispose()}};async function tt(e){let t=await h0.loadGraphModel(e.body.modelPath),n=new B1(t);return e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new et(n)}var _t={};Q(_t,{HandPose:()=>ht,load:()=>it});var ot=k(N());var g=k(N());var i0=k(N());function De(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Re(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function a0(e,t,n){let c=t.shape[1],r=t.shape[2],A=[[e.startPoint[1]/c,e.startPoint[0]/r,e.endPoint[1]/c,e.endPoint[0]/r]];return i0.image.cropAndResize(t,A,[0],n)}function x0(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],c=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(A=>[A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:c,palmLandmarks:r,confidence:e.confidence}}function Ge(e,t=1.5){let n=Re(e),c=De(e),r=[t*c[0]/2,t*c[1]/2],A=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}function Qe(e){let t=Re(e),n=De(e),r=Math.max(...n)/2,A=[t[0]-r,t[1]-r],_=[t[0]+r,t[1]+r];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}var nt=class{constructor(t,n,c){this.model=t,this.anchors=c.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=g.tensor2d(this.anchors),this.inputSize=n,this.inputSizeTensor=g.tensor1d([n,n]),this.doubleInputSizeTensor=g.tensor1d([n*2,n*2])}normalizeBoxes(t){return g.tidy(()=>{let 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n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A}}};var 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u0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],b0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var U;async function xt(e){return U||(U=await ae.loadGraphModel(e.body.modelPath),U.width=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[2].size),U.height=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),U}async function yt(e,t){if(!U||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},c=ae.image.resizeBilinear(e,[U.width,U.height],!1),r=ae.div(c,[255]);c.dispose();let A;if(t.profile){let i=await ae.profile(()=>U.predict(r));A=i.result.find(a=>a.size===195||a.size===155).dataSync(),i.result.forEach(a=>a.dispose()),B("blazepose",i)}else{let i=await U.predict(r);A=i.find(a=>a.size===195||a.size===155).dataSync(),i.forEach(a=>a.dispose())}r.dispose();let _=[],o=A.length===195?u0:b0,s=5;for(let 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sn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],hn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],an=[33,133,362,263,1,78,308],wr=sn.map(e=>H1[e]),lr=hn.map(e=>H1[e]),dr=an.map(e=>H1[e]);var X1=Ae.leftEyeLower0,F1=Ae.rightEyeLower0,be={leftBounds:[X1[0],X1[X1.length-1]],rightBounds:[F1[0],F1[F1.length-1]]},De={count:468,mouth:13,symmetryLine:[13,Ae.midwayBetweenEyes[0]]},Kt={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ge(e,t,n,c){for(let r=0;r<V1.length;r++){let{key:A,indices:_}=V1[r],o=Ae[`${n}${A}`];if(!c||c.includes(A))for(let s=0;s<_.length;s++){let h=_[s];e[o[s]]=[t[h][0],t[h][1],(t[h][2]+e[o[s]][2])/2]}}}var q1=class{constructor(t,n,c){var r,A;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=c,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((A=t==null?void 0:t.model)==null?void 0:A.inputs[0].shape[2]),this.irisSize=(c==null?void 0:c.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,c,r){let A=Me({startPoint:n.startPoint,endPoint:n.endPoint}),_=t.map(i=>[A[0]/this.meshSize*(i[0]-this.meshSize/2),A[1]/this.meshSize*(i[1]-this.meshSize/2),i[2]]),o=c!==0?Ue(c,[0,0]):Ke,s=c!==0?_.map(i=>[...Jt(i,o),i[2]]):_,h=c!==0?Yt(r):Ke,x=[...pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(i=>[i[0]+he(x,h[0]),i[1]+he(x,h[1]),i[2]])}getLeftToRightEyeDepthDifference(t){let n=t[be.leftBounds[0]][2],c=t[be.rightBounds[0]][2];return n-c}getEyeBox(t,n,c,r,A=!1){let _=Je(Ye(this.calculateLandmarksBoundingBox([t[c],t[r]]),this.irisEnlarge)),o=Me(_),s=X.image.cropAndResize(n,[[_.startPoint[1]/this.meshSize,_.startPoint[0]/this.meshSize,_.endPoint[1]/this.meshSize,_.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return A&&X.ENV.flags.IS_BROWSER&&(s=X.image.flipLeftRight(s)),{box:_,boxSize:o,crop:s}}getEyeCoords(t,n,c,r=!1){let A=[];for(let _=0;_<ge.numCoordinates;_++){let o=t[_*3],s=t[_*3+1],h=t[_*3+2];A.push([(r?1-o/this.irisSize:o/this.irisSize)*c[0]+n.startPoint[0],s/this.irisSize*c[1]+n.startPoint[1],h])}return{rawCoords:A,iris:A.slice(ge.index)}}getAdjustedIrisCoords(t,n,c){let r=t[Ae[`${c}EyeUpper0`][ge.upperCenter]][2],A=t[Ae[`${c}EyeLower0`][ge.lowerCenter]][2],_=(r+A)/2;return n.map((o,s)=>{let h=_;return s===2?h=r:s===4&&(h=A),[o[0],o[1],h]})}async predict(t,n){let c=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let _ of r.boxes)this.storedBoxes.push({startPoint:_.box.startPoint.dataSync(),endPoint:_.box.endPoint.dataSync(),landmarks:_.landmarks,confidence:_.confidence});this.storedBoxes.length>0&&(c=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),c){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let _=0;_<this.storedBoxes.length;_++){let o=qt({startPoint:this.storedBoxes[_].startPoint,endPoint:this.storedBoxes[_].endPoint},r.scaleFactor),s=Ye(o),h=Je(s),x=this.storedBoxes[_].landmarks.arraySync(),i=this.storedBoxes[_].confidence;this.storedBoxes[_]={...h,confidence:i,landmarks:x}}}r&&r.boxes&&r.boxes.forEach(_=>{_.box.startPoint.dispose(),_.box.endPoint.dispose(),_.landmarks.dispose()});let A=X.tidy(()=>this.storedBoxes.map((_,o)=>{let s=_.confidence,h,x=0,i;if(n.face.detector.rotation&&n.face.mesh.enabled&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=De.count?De.symmetryLine:Kt.symmetryLine;x=L1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),v=[p[0]/t.shape[2],p[1]/t.shape[1]],E=X.image.rotateWithOffset(t,x,0,v);i=Ue(-x,p),n.face.mesh.enabled?h=ue({startPoint:_.startPoint,endPoint:_.endPoint},E,[this.meshSize,this.meshSize]).div(255):h=ue({startPoint:_.startPoint,endPoint:_.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{i=Ke;let m=t.clone();n.face.mesh.enabled?h=ue({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.meshSize,this.meshSize]).div(255):h=ue({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,boxConfidence:s,confidence:_.confidence,image:h};let[,y,f]=this.meshDetector.predict(h),d=y.dataSync()[0];if(d<n.face.detector.minConfidence)return null;let P=X.reshape(f,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:m,boxSize:l,crop:p}=this.getEyeBox(P,h,be.leftBounds[0],be.leftBounds[1],!0),{box:v,boxSize:E,crop:C}=this.getEyeBox(P,h,be.rightBounds[0],be.rightBounds[1]),te=this.irisModel.predict(X.concat([p,C])).dataSync(),f1=te.slice(0,ge.numCoordinates*3),{rawCoords:Ne,iris:ye}=this.getEyeCoords(f1,m,l,!0),Z0=te.slice(ge.numCoordinates*3),{rawCoords:Et,iris:L0}=this.getEyeCoords(Z0,v,E),St=this.getLeftToRightEyeDepthDifference(P);Math.abs(St)<30?(Ge(P,Ne,"left",null),Ge(P,Et,"right",null)):St<1?Ge(P,Ne,"left",["EyeUpper0","EyeLower0"]):Ge(P,Et,"right",["EyeUpper0","EyeLower0"]);let V0=this.getAdjustedIrisCoords(P,ye,"left"),H0=this.getAdjustedIrisCoords(P,L0,"right");P=P.concat(V0).concat(H0)}let b=this.transformRawCoords(P,_,x,i);_=Ye(this.calculateLandmarksBoundingBox(b),1.5);let M=X.tensor2d(b);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=De.count?De.symmetryLine:Kt.symmetryLine;x=L1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),v=[p[0]/t.shape[2],p[1]/t.shape[1]],E=X.image.rotateWithOffset(t,x,0,v);i=Ue(-x,p),h=ue({startPoint:_.startPoint,endPoint:_.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let W={coords:M,box:_,faceConfidence:d,boxConfidence:s,image:h,rawCoords:P},Y=Je(_);return this.storedBoxes[o]={...Y,landmarks:b,confidence:_.confidence,faceConfidence:d},W}));return A=A.filter(_=>_!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(_=>_.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=A.length,A}calculateLandmarksBoundingBox(t){let n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A,landmarks:t}}};var Y1=class{constructor(t,n,c,r){this.facePipeline=new q1(t,n,c),this.config=r}async estimateFaces(t,n){let c=await this.facePipeline.predict(t,n),r=[];for(let A of c||[]){if(A.isDisposedInternal)continue;let _=A.coords?A.coords.arraySync():[],o=_.map(i=>[i[0]/t.shape[2],i[1]/t.shape[1],i[2]/this.facePipeline.meshSize]),s={};if(_&&_.length>0)for(let i of Object.keys(Ae))s[i]=Ae[i].map(y=>_[y]);let h=A.box?[Math.max(0,A.box.startPoint[0]),Math.max(0,A.box.startPoint[1]),Math.min(t.shape[1],A.box.endPoint[0])-Math.max(0,A.box.startPoint[0]),Math.min(t.shape[2],A.box.endPoint[1])-Math.max(0,A.box.startPoint[1])]:0,x=A.box?[A.box.startPoint[0]/t.shape[2],A.box.startPoint[1]/t.shape[1],(A.box.endPoint[0]-A.box.startPoint[0])/t.shape[2],(A.box.endPoint[1]-A.box.startPoint[1])/t.shape[1]]:[];r.push({confidence:A.faceConfidence||A.boxConfidence||0,boxConfidence:A.boxConfidence,faceConfidence:A.faceConfidence,box:h,boxRaw:x,mesh:_,meshRaw:o,annotations:s,image:A.image?A.image.clone():null}),A.coords&&A.coords.dispose(),A.image&&A.image.dispose()}return r}},de=[null,null,null];async function J1(e){de=await Promise.all([!de[0]&&e.face.enabled?Xt(e):null,!de[1]&&e.face.mesh.enabled?B1.loadGraphModel(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!de[2]&&e.face.iris.enabled?B1.loadGraphModel(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new Y1(de[0],de[1],de[2],e);return e.face.mesh.enabled&&e.debug&&u(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&u(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var xn=le;var rt={};Q(rt,{PoseNet:()=>ct,load:()=>At});var x0=O(N());var Ut=O(N());function yn(e){let[t,n,c,r]=e;return{offsets:t,heatmap:n,displacementFwd:c,displacementBwd:r}}var K1=class{constructor(t){this.model=t}predict(t){return Ut.tidy(()=>{let c=t.toFloat().div(127.5).sub(1).expandDims(0),A=this.model.predict(c).map(o=>o.squeeze([0])),_=yn(A);return{heatmapScores:_.heatmap.sigmoid(),offsets:_.offsets,displacementFwd:_.displacementFwd,displacementBwd:_.displacementBwd}})}dispose(){this.model.dispose()}};function U1(e){return Math.floor(e/2)}var D1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(U1(t),t);)this.exchange(t,U1(t)),t=U1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let c=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=c}};function wn(e,t,n,c,r,A){let[_,o]=A.shape,s=!0,h=Math.max(n-r,0),x=Math.min(n+r+1,_);for(let i=h;i<x;++i){let y=Math.max(c-r,0),f=Math.min(c+r+1,o);for(let d=y;d<f;++d)if(A.get(i,d,e)>t){s=!1;break}if(!s)break}return s}function Dt(e,t,n){let[c,r,A]=n.shape,_=new D1(c*r*A,({score:o})=>o);for(let o=0;o<c;++o)for(let s=0;s<r;++s)for(let h=0;h<A;++h){let x=n.get(o,s,h);x<e||wn(h,x,o,s,t,n)&&_.enqueue({score:x,part:{heatmapY:o,heatmapX:s,id:h}})}return _}var Te=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Pe=Te.length,Re=Te.reduce((e,t,n)=>(e[t]=n,e),{}),ln=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Sr=ln.map(([e,t])=>[Re[e],Re[t]]),Gt=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function G1(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Pe)}}function Qe(e,t,n){let{heatmapY:c,heatmapX:r,id:A}=e,{y:_,x:o}=G1(c,r,A,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+_}}function Q1(e,t,n){return e<t?t:e>n?n:e}function Qt(e,t,n,c){let r=n-e,A=c-t;return r*r+A*A}function $1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var J=O(N());function $t(e,t){let n=t.shape[0],c=new Float32Array(n);for(let r=0;r<n;r++){let A=t.get(r,0),_=t.get(r,1);c[r]=e.get(A,_,r)}return c}function dn(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Pe)}}function fn(e,t){let n=[];for(let c=0;c<Pe;c++){let r=e.get(c,0).valueOf(),A=e.get(c,1).valueOf(),{x:_,y:o}=dn(r,A,c,t);n.push(o),n.push(_)}return J.tensor2d(n,[Pe,2])}function e0(e,t,n){return J.tidy(()=>e.toTensor().mul(J.scalar(t,"int32")).toFloat().add(fn(e,n)))}function mn(e,t){return J.tidy(()=>{let n=e.div(J.scalar(t,"int32"));return e.sub(n.mul(J.scalar(t,"int32")))})}function t0(e){let[t,n,c]=e.shape;return J.tidy(()=>{let A=e.reshape([t*n,c]).argMax(0),_=A.div(J.scalar(n,"int32")).expandDims(1),o=mn(A,n).expandDims(1);return J.concat([_,o],1)})}var n0=Gt.map(([e,t])=>[Re[e],Re[t]]),et=n0.map(([,e])=>e),r0=n0.map(([e])=>e),pn=16;function un(e,t,n){let c=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,c+e)}}function tt(e,t,n,c){return{y:Q1(Math.round(e.y/t),0,n-1),x:Q1(Math.round(e.x/t),0,c-1)}}function c0(e,t,n,c,r,A,_,o=2){let[s,h]=c.shape,x=tt(t.position,A,s,h),i=un(e,x,_),f=$1(t.position,i);for(let P=0;P<o;P++){let b=tt(f,A,s,h),M=G1(b.y,b.x,n,r);f=$1({x:b.x*A,y:b.y*A},{x:M.x,y:M.y})}let d=tt(f,A,s,h),a=c.get(d.y,d.x,n);return{position:f,part:Te[n],score:a}}function A0(e,t,n,c,r,A){let _=t.shape[2],o=et.length,s=new Array(_),{part:h,score:x}=e,i=Qe(h,c,n);s[h.id]={score:x,part:Te[h.id],position:i};for(let y=o-1;y>=0;--y){let f=et[y],d=r0[y];s[f]&&!s[d]&&(s[d]=c0(y,s[f],d,t,n,c,A))}for(let y=0;y<o;++y){let f=r0[y],d=et[y];s[f]&&!s[d]&&(s[d]=c0(y,s[f],d,t,n,c,r))}return s}async function _0(e,t,n){let c=0,r=t0(e),A=await Promise.all([e.buffer(),t.buffer(),r.buffer()]),_=A[0],o=A[1],s=A[2],h=e0(s,pn,o),x=await h.buffer(),y=Array.from($t(_,s)).map((d,a)=>(c+=d,{position:{y:x.get(a,0),x:x.get(a,1)},part:Te[a],score:d})),f=y.filter(d=>d.score>n);return r.dispose(),h.dispose(),{keypoints:f,score:c/y.length}}var bn=1,o0=16;function s0(e,t,{x:n,y:c},r){return e.some(({keypoints:A})=>{let _=A[r].position;return Qt(c,n,_.y,_.x)<=t})}function gn(e,t,n){return n.reduce((r,{position:A,score:_},o)=>(s0(e,t,A,o)||(r+=_),r),0)/n.length}function h0(e,t,n,c,r,A,_){let o=[],s=Dt(_,bn,e),h=r^2;for(;o.length<A&&!s.empty();){let x=s.dequeue(),i=Qe(x.part,o0,t);if(s0(o,h,i,x.part.id))continue;let y=A0(x,e,t,o0,n,c),f=gn(o,h,y);f>_&&o.push({keypoints:y,score:f})}return o}async function i0(e){return Promise.all(e.map(t=>t.buffer()))}function Tn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:c,part:r,position:A})=>({score:c,part:r,position:{x:Math.trunc(A.x*n),y:Math.trunc(A.y*t)}}))}}function a0(e,[t,n]){let c=e.squeeze(0),r=c.resizeBilinear([t,n]);return c.dispose(),r}function nt(e,[t,n],[c,r]){return e.map(_=>Tn(_,t/c,n/r))}async function Pn(e,t,n,c){return new Promise(async r=>{let A=await i0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),_=A[0],o=A[1],s=A[2],h=A[3],x=await h0(_,o,s,h,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),i=nt(x,[e.shape[1],e.shape[2]],[c,c]);r(i)})}async function vn(e,t,n,c){return new Promise(async r=>{let A=await _0(t.heatmapScores,t.offsets,n.body.scoreThreshold),_=nt([A],[e.shape[1],e.shape[2]],[c,c]);r(_)})}var ct=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let c=a0(t,[this.inputSize,this.inputSize]),r=this.baseModel.predict(c,n),A=n.body.maxDetections<2?await vn(t,r,n,this.inputSize):await Pn(t,r,n,this.inputSize);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),c.dispose(),A}dispose(){this.baseModel.dispose()}};async function At(e){let t=await x0.loadGraphModel(e.body.modelPath),n=new K1(t);return e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new ct(n)}var it={};Q(it,{HandPose:()=>yt,load:()=>wt});var at=O(N());var g=O(N());var y0=O(N());function $e(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ee(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function w0(e,t,n){let c=t.shape[1],r=t.shape[2],A=[[e.startPoint[1]/c,e.startPoint[0]/r,e.endPoint[1]/c,e.endPoint[0]/r]];return y0.image.cropAndResize(t,A,[0],n)}function l0(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],c=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(A=>[A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:c,palmLandmarks:r,confidence:e.confidence}}function e1(e,t=1.5){let n=Ee(e),c=$e(e),r=[t*c[0]/2,t*c[1]/2],A=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}function t1(e){let t=Ee(e),n=$e(e),r=Math.max(...n)/2,A=[t[0]-r,t[1]-r],_=[t[0]+r,t[1]+r];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}var _t=class{constructor(t,n,c){this.model=t,this.anchors=c.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=g.tensor2d(this.anchors),this.inputSize=n,this.inputSizeTensor=g.tensor1d([n,n]),this.doubleInputSizeTensor=g.tensor1d([n*2,n*2])}normalizeBoxes(t){return g.tidy(()=>{let 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n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A}}};var 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T0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],P0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var U;async function dt(e){return U||(U=await ae.loadGraphModel(e.body.modelPath),U.width=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[2].size),U.height=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),U}async function ft(e,t){if(!U||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},c=ae.image.resizeBilinear(e,[U.width,U.height],!1),r=ae.div(c,[255]);c.dispose();let A;if(t.profile){let h=await ae.profile(()=>U.predict(r));A=h.result.find(x=>x.size===195||x.size===155).dataSync(),h.result.forEach(x=>x.dispose()),q("blazepose",h)}else{let h=await U.predict(r);A=h.find(x=>x.size===195||x.size===155).dataSync(),h.forEach(x=>x.dispose())}r.dispose();let _=[],o=A.length===195?T0:P0,s=5;for(let 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+`),i.pixelate=function(m){let l=m/o,p=m/s,v=Y(i.pixelate.SHADER);a.uniform2f(v.uniform.size,l,p),W()},i.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
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h;if(V.data){let i=[V.height,V.width,3];h=ee.tensor3d(V.data,i,"int32")}else if(t.backend==="webgl"||V instanceof ImageData)h=ee.browser.fromPixels(V);else{let i=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(_,o):document.createElement("canvas");i.width=_,i.height=o;let y=i.getContext("2d");y==null||y.drawImage(V,0,0);let f=y==null?void 0:y.getImageData(0,0,_,o);h=ee.browser.fromPixels(f)}let x=h.toFloat();n=x.expandDims(0),h.dispose(),x.dispose()}let c=t.filter.return?V:null;return{tensor:n,canvas:c}}var vt={};Q(vt,{all:()=>In,body:()=>W0,canvas:()=>kn,drawOptions:()=>w,face:()=>j0,gesture:()=>S0,hand:()=>N0,object:()=>O0});var j={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var w={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function h1(e,t,n,c=null){e.fillStyle=w.useDepth&&c?`rgba(${127.5+2*(c||0)}, ${127.5-2*(c||0)}, 255, 0.3)`:w.color,e.beginPath(),e.arc(t,n,w.pointSize,0,2*Math.PI),e.fill()}function ve(e,t,n,c,r){if(e.beginPath(),w.useCurves){let A=(t+t+c)/2,_=(n+n+r)/2;e.ellipse(A,_,c/2,r/2,0,0,2*Math.PI)}else e.lineWidth=w.lineWidth,e.moveTo(t+w.roundRect,n),e.lineTo(t+c-w.roundRect,n),e.quadraticCurveTo(t+c,n,t+c,n+w.roundRect),e.lineTo(t+c,n+r-w.roundRect),e.quadraticCurveTo(t+c,n+r,t+c-w.roundRect,n+r),e.lineTo(t+w.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-w.roundRect),e.lineTo(t,n+w.roundRect),e.quadraticCurveTo(t,n,t+w.roundRect,n),e.closePath();e.stroke()}function zt(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=w.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:w.color,e.fillStyle=w.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:w.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),w.fillPolygons&&(e.closePath(),e.fill())}}function i1(e,t=[]){if(!(t===void 0||t.length===0)){if(!w.useCurves||t.length<=2){zt(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let c=(t[n][0]+t[n+1][0])/2,r=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],c,r)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),w.fillPolygons&&(e.closePath(),e.fill())}}async function S0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=w.font,n.fillStyle=w.color;let c=1;for(let r=0;r<t.length;r++){let A=[],_=[];if([A,_]=Object.entries(t[r]),_.length>1&&_[1].length>0){let o=A[1]>0?`#${A[1]}`:"",s=`${A[0]} ${o}: ${_[1]}`;w.shadowColor&&w.shadowColor!==""&&(n.fillStyle=w.shadowColor,n.fillText(s,8,2+c*w.lineHeight)),n.fillStyle=w.labelColor,n.fillText(s,6,0+c*w.lineHeight),c+=1}}}async function j0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let c of t){n.font=w.font,n.strokeStyle=w.color,n.fillStyle=w.color,w.drawBoxes&&(w.useRawBoxes?ve(n,e.width*c.boxRaw[0],e.height*c.boxRaw[1],e.width*c.boxRaw[2],e.height*c.boxRaw[3]):ve(n,c.box[0],c.box[1],c.box[2],c.box[3]));let r=[];if(r.push(`face confidence: ${Math.trunc(100*c.confidence)}%`),c.genderConfidence&&r.push(`${c.gender||""} ${Math.trunc(100*c.genderConfidence)}% confident`),c.age&&r.push(`age: ${c.age||""}`),c.iris&&r.push(`iris distance: ${c.iris}`),c.emotion&&c.emotion.length>0){let A=c.emotion.map(_=>`${Math.trunc(100*_.score)}% ${_.emotion}`);r.push(A.join(" "))}c.angle&&c.angle.roll&&r.push(`roll: ${Math.trunc(100*c.angle.roll)/100} yaw:${Math.trunc(100*c.angle.yaw)/100} pitch:${Math.trunc(100*c.angle.pitch)/100}`),r.length===0&&r.push("face"),n.fillStyle=w.color;for(let A=r.length-1;A>=0;A--){let _=Math.max(c.box[0],0),o=A*w.lineHeight+c.box[1];w.shadowColor&&w.shadowColor!==""&&(n.fillStyle=w.shadowColor,n.fillText(r[A],_+5,o+16)),n.fillStyle=w.labelColor,n.fillText(r[A],_+4,o+15)}if(n.lineWidth=1,c.mesh&&c.mesh.length>0){if(w.drawPoints)for(let A of c.mesh)h1(n,A[0],A[1],A[2]);if(w.drawPolygons){n.lineWidth=1;for(let A=0;A<le.length/3;A++){let _=[le[A*3+0],le[A*3+1],le[A*3+2]].map(o=>c.mesh[o]);zt(n,_)}if(c.annotations&&c.annotations.leftEyeIris){n.strokeStyle=w.useDepth?"rgba(255, 200, 255, 0.3)":w.color,n.beginPath();let A=Math.abs(c.annotations.leftEyeIris[3][0]-c.annotations.leftEyeIris[1][0])/2,_=Math.abs(c.annotations.leftEyeIris[4][1]-c.annotations.leftEyeIris[2][1])/2;n.ellipse(c.annotations.leftEyeIris[0][0],c.annotations.leftEyeIris[0][1],A,_,0,0,2*Math.PI),n.stroke(),w.fillPolygons&&(n.fillStyle=w.useDepth?"rgba(255, 255, 200, 0.3)":w.color,n.fill())}if(c.annotations&&c.annotations.rightEyeIris){n.strokeStyle=w.useDepth?"rgba(255, 200, 255, 0.3)":w.color,n.beginPath();let A=Math.abs(c.annotations.rightEyeIris[3][0]-c.annotations.rightEyeIris[1][0])/2,_=Math.abs(c.annotations.rightEyeIris[4][1]-c.annotations.rightEyeIris[2][1])/2;n.ellipse(c.annotations.rightEyeIris[0][0],c.annotations.rightEyeIris[0][1],A,_,0,0,2*Math.PI),n.stroke(),w.fillPolygons&&(n.fillStyle=w.useDepth?"rgba(255, 255, 200, 0.3)":w.color,n.fill())}}}}}var xe=[];async function W0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let c=0;c<t.length;c++){if(!xe[c]&&w.bufferedOutput&&(xe[c]={...t[c]}),n.strokeStyle=w.color,n.lineWidth=w.lineWidth,w.drawPoints)for(let r=0;r<t[c].keypoints.length;r++)n.fillStyle=w.useDepth&&t[c].keypoints[r].position.z?`rgba(${127.5+2*t[c].keypoints[r].position.z}, ${127.5-2*t[c].keypoints[r].position.z}, 255, 0.5)`:w.color,w.bufferedOutput?(xe[c].keypoints[r][0]=(xe[c].keypoints[r][0]+t[c].keypoints[r].position.x)/2,xe[c].keypoints[r][1]=(xe[c].keypoints[r][1]+t[c].keypoints[r].position.y)/2,h1(n,xe[c].keypoints[r][0],xe[c].keypoints[r][1])):h1(n,t[c].keypoints[r].position.x,t[c].keypoints[r].position.y);if(w.drawLabels&&(n.font=w.font,t[c].keypoints))for(let r of t[c].keypoints)n.fillStyle=w.useDepth&&r.position.z?`rgba(${127.5+2*r.position.z}, ${127.5-2*r.position.z}, 255, 0.5)`:w.color,n.fillText(`${r.part}`,r.position.x+4,r.position.y+4);if(w.drawPolygons&&t[c].keypoints){let r,A=[];A.length=0,r=t[c].keypoints.find(_=>_.part==="leftShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),A.length===5&&zt(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="leftHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftKnee"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftAnkle"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftHeel"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftFoot"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="rightHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightKnee"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightAnkle"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightHeel"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightFoot"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="leftShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftElbow"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftWrist"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftPalm"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="rightShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightElbow"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightWrist"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightPalm"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A)}}}}async function N0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=w.font;for(let c of t){if(w.drawBoxes&&(n.strokeStyle=w.color,n.fillStyle=w.color,w.useRawBoxes?ve(n,e.width*c.boxRaw[0],e.height*c.boxRaw[1],e.width*c.boxRaw[2],e.height*c.boxRaw[3]):ve(n,c.box[0],c.box[1],c.box[2],c.box[3]),w.drawLabels&&(w.shadowColor&&w.shadowColor!==""&&(n.fillStyle=w.shadowColor,n.fillText("hand",c.box[0]+3,1+c.box[1]+w.lineHeight,c.box[2])),n.fillStyle=w.labelColor,n.fillText("hand",c.box[0]+2,0+c.box[1]+w.lineHeight,c.box[2])),n.stroke()),w.drawPoints&&c.landmarks&&c.landmarks.length>0)for(let r of c.landmarks)n.fillStyle=w.useDepth?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.5)`:w.color,h1(n,r[0],r[1]);if(w.drawPolygons){let r=A=>{if(!!A)for(let _=0;_<A.length;_++)n.lineWidth=w.lineWidth,n.beginPath(),n.strokeStyle=w.useDepth?`rgba(${127.5+2*A[_][2]}, ${127.5-2*A[_][2]}, 255, 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backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&u("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let c=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),r=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&u(`wasm execution: ${c?"SIMD":"no SIMD"} ${r?"multithreaded":"singlethreaded"}`),c||u("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&Nt();try{await this.tf.setBackend(this.config.backend)}catch(c){u("error: cannot set backend:",this.config.backend,c)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let c=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${c.getParameter(c.VERSION)} renderer:${c.getParameter(c.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-n)}});i1.set(this,async()=>{let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(_=>_.blob()),n,c;switch(this.config.warmup){case"face":n=await t(o1);break;case"full":n=await t(s1);break;default:n=null}if(n){let r=await createImageBitmap(n);c=await this.detect(r,this.config),r.close()}return c});a1.set(this,async()=>new Promise(t=>{let n,c=0;switch(this.config.warmup){case"face":c=256,n="data:image/jpeg;base64,"+o1;break;case"full":case"body":c=1200,n="data:image/jpeg;base64,"+s1;break;default:n=null}let r=new Image;r.onload=async()=>{let A=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(c,c):document.createElement("canvas");A.width=r.naturalWidth,A.height=r.naturalHeight;let _=A.getContext("2d");_==null||_.drawImage(r,0,0);let o=await this.detect(A,this.config);t(o)},n?r.src=n:t(null)}));x1.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?t(o1):t(s1),c=Tt.node.decodeJpeg(n),r=c.expandDims(0);this.tf.dispose(c);let A=await this.detect(r,this.config);return this.tf.dispose(r),A});this.tf=Tt,this.draw=bt,this.version=k0,this.config=fe(j,t),this.state="idle",ye(this,ve,0),ye(this,Ee,!1),ye(this,Se,!1),ye(this,de,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>ut(n,this.config),this.classes={facemesh:H1,age:d1,gender:p1,emotion:T1,faceres:E1,body:this.config.body.modelPath.includes("posenet")?$1:at,hand:_t,nanodet:dt},this.sysinfo=St()}profileData(){return this.config.profile?l1:{}}similarity(t,n){return this.config.face.description.enabled?j1(t,n):this.config.face.embedding.enabled?kt(t,n):0}enhance(t){return W1(t)}match(t,n,c=0){return Ot(t,n,c)}async load(t={}){this.state="load";let n=v();t&&(this.config=fe(this.config,t)),K(this,de)&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version_core}`),this.config.debug&&u("platform:",this.sysinfo.platform),this.config.debug&&u("agent:",this.sysinfo.agent),await K(this,je).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?q1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?f1(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?g1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?z1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?M1(this.config):null),this.models.handpose||(this.config.hand.enabled?it(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?tt(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?xt(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?wt(this.config):null),this.models.nanodet||(this.config.object.enabled?pt(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?S1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await q1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await f1(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await g1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await z1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await M1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await it(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await tt(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await xt(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await wt(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await pt(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await S1(this.config))),K(this,de)&&(this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ye(this,de,!1));let c=Math.trunc(v()-n);c>(this.perf.load||0)&&(this.perf.load=c)}async detect(t,n={}){return new Promise(async c=>{var x,z,b,W;this.state="config";let r;this.config=fe(this.config,n),this.state="check";let A=K(this,h1).call(this,t);A&&(u(A,t),c({error:A}));let _=v();await K(this,je).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=v();let o=ut(t,this.config);if(!o||!o.tensor){u("could not convert input to tensor"),c({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-r),this.analyze("Get Image:");let s,i,a,h,y;this.config.async?(a=this.config.face.enabled?N1(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=v(),a=this.config.face.enabled?await N1(this,o.tensor):[],y=Math.trunc(v()-r),y>0&&(this.perf.face=y)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?(x=this.models.posenet)==null?void 0:x.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?yt(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?lt(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=v(),this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?await((z=this.models.posenet)==null?void 0:z.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?await yt(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?await lt(o.tensor,this.config):[]),y=Math.trunc(v()-r),y>0&&(this.perf.body=y)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(i=this.config.hand.enabled?(b=this.models.handpose)==null?void 0:b.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=v(),i=this.config.hand.enabled?await((W=this.models.handpose)==null?void 0:W.estimateHands(o.tensor,this.config)):[],y=Math.trunc(v()-r),y>0&&(this.perf.hand=y)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?mt(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=v(),h=this.config.object.enabled?await mt(o.tensor,this.config):[],y=Math.trunc(v()-r),y>0&&(this.perf.object=y)),this.analyze("End Object:"),this.config.async&&([a,s,i,h]=await Promise.all([a,s,i,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let d=[];this.config.gesture.enabled&&(r=v(),d=[...v0(a),...P0(s),...M0(i),...z0(a)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-r)),this.perf.total=Math.trunc(v()-_),this.state="idle";let f={face:a,body:s,hand:i,gesture:d,object:h,performance:this.perf,canvas:o.canvas};c(f)})}async warmup(t={}){let n=v();t&&(this.config=fe(this.config,t));let c=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await K(this,i1).call(this):typeof Image!="undefined"?r=await K(this,a1).call(this):r=await K(this,x1).call(this),this.config.videoOptimized=c;let A=v();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(A-n),"ms",r),r}};ve=new WeakMap,Ee=new WeakMap,Se=new WeakMap,de=new WeakMap,h1=new WeakMap,je=new WeakMap,i1=new WeakMap,a1=new WeakMap,x1=new WeakMap;0&&(module.exports={Human});
+2Q==`;var k0="1.2.5";var ze,Se,je,fe,y1,We,w1,l1,d1,I0=class{constructor(t={}){ze.set(this,void 0);Se.set(this,void 0);je.set(this,void 0);fe.set(this,void 0);this.analyze=(...t)=>{if(!K(this,Se))return;let n=this.tf.engine().state.numTensors,c=K(this,ze);we(this,ze,n);let r=n-c;r!==0&&u(...t,r)};y1.set(this,t=>{if(!K(this,je))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Mt.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});We.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=z();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&u("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&u("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let c=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),r=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&u(`wasm execution: ${c?"SIMD":"no SIMD"} ${r?"multithreaded":"singlethreaded"}`),c||u("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&It();try{await this.tf.setBackend(this.config.backend)}catch(c){u("error: cannot set backend:",this.config.backend,c)}}if(this.tf.enableProdMode(),this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let c=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${c.getParameter(c.VERSION)} renderer:${c.getParameter(c.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(z()-n)}});w1.set(this,async()=>{let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(_=>_.blob()),n,c;switch(this.config.warmup){case"face":n=await t(a1);break;case"full":n=await t(x1);break;default:n=null}if(n){let r=await createImageBitmap(n);c=await this.detect(r,this.config),r.close()}return c});l1.set(this,async()=>new Promise(t=>{let n,c=0;switch(this.config.warmup){case"face":c=256,n="data:image/jpeg;base64,"+a1;break;case"full":case"body":c=1200,n="data:image/jpeg;base64,"+x1;break;default:n=null}let r=new Image;r.onload=async()=>{let A=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(c,c):document.createElement("canvas");A.width=r.naturalWidth,A.height=r.naturalHeight;let _=A.getContext("2d");_==null||_.drawImage(r,0,0);let o=await this.detect(A,this.config);t(o)},n?r.src=n:t(null)}));d1.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?t(a1):t(x1),c=Mt.node.decodeJpeg(n),r=c.expandDims(0);this.tf.dispose(c);let A=await this.detect(r,this.config);return this.tf.dispose(r),A});this.tf=Mt,this.draw=vt,this.version=k0,this.config=me(j,t),this.state="idle",we(this,ze,0),we(this,Se,!1),we(this,je,!1),we(this,fe,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>Pt(n,this.config),this.classes={facemesh:C1,age:u1,gender:g1,emotion:M1,faceres:N1,body:this.config.body.modelPath.includes("posenet")?rt:lt,hand:it,nanodet:ut},this.sysinfo=Nt()}profileData(){return this.config.profile?ke:{}}similarity(t,n){return this.config.face.description.enabled?k1(t,n):this.config.face.embedding.enabled?Zt(t,n):0}enhance(t){return I1(t)}match(t,n,c=0){return Lt(t,n,c)}async load(t={}){this.state="load";let n=z();t&&(this.config=me(this.config,t)),K(this,fe)&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version_core}`),this.config.debug&&u("platform:",this.sysinfo.platform),this.config.debug&&u("agent:",this.sysinfo.agent),await K(this,We).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?J1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?b1(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?z1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?S1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?j1(this.config):null),this.models.handpose||(this.config.hand.enabled?wt(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?At(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?dt(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?mt(this.config):null),this.models.nanodet||(this.config.object.enabled?gt(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?O1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await J1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await b1(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await z1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await S1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await j1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await wt(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await At(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await dt(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await mt(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await gt(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await O1(this.config))),K(this,fe)&&(this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),we(this,fe,!1));let c=Math.trunc(z()-n);c>(this.perf.load||0)&&(this.perf.load=c)}async detect(t,n={}){return new Promise(async c=>{var a,P,b,M;this.state="config";let r;this.config=me(this.config,n),this.state="check";let A=K(this,y1).call(this,t);A&&(u(A,t),c({error:A}));let _=z();await K(this,We).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=z();let o=Pt(t,this.config);if(!o||!o.tensor){u("could not convert input to tensor"),c({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(z()-r),this.analyze("Get Image:");let s,h,x,i,y;this.config.async?(x=this.config.face.enabled?Z1(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=z(),x=this.config.face.enabled?await Z1(this,o.tensor):[],y=Math.trunc(z()-r),y>0&&(this.perf.face=y)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?(a=this.models.posenet)==null?void 0:a.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?ft(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?pt(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=z(),this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?await((P=this.models.posenet)==null?void 0:P.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?await ft(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?await pt(o.tensor,this.config):[]),y=Math.trunc(z()-r),y>0&&(this.perf.body=y)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(b=this.models.handpose)==null?void 0:b.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=z(),h=this.config.hand.enabled?await((M=this.models.handpose)==null?void 0:M.estimateHands(o.tensor,this.config)):[],y=Math.trunc(z()-r),y>0&&(this.perf.hand=y)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(i=this.config.object.enabled?Tt(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=z(),i=this.config.object.enabled?await Tt(o.tensor,this.config):[],y=Math.trunc(z()-r),y>0&&(this.perf.object=y)),this.analyze("End Object:"),this.config.async&&([x,s,h,i]=await Promise.all([x,s,h,i])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(r=z(),f=[...z0(x),...v0(s),...R0(h),...M0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(z()-r)),this.perf.total=Math.trunc(z()-_),this.state="idle";let d={face:x,body:s,hand:h,gesture:f,object:i,performance:this.perf,canvas:o.canvas};c(d)})}async warmup(t={}){let n=z();t&&(this.config=me(this.config,t));let c=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await K(this,w1).call(this):typeof Image!="undefined"?r=await K(this,l1).call(this):r=await K(this,d1).call(this),this.config.videoOptimized=c;let A=z();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(A-n),"ms",r),r}};ze=new WeakMap,Se=new WeakMap,je=new WeakMap,fe=new WeakMap,y1=new WeakMap,We=new WeakMap,w1=new WeakMap,l1=new WeakMap,d1=new WeakMap;0&&(module.exports={Human});
 //# sourceMappingURL=human.node-gpu.js.map
diff --git a/dist/human.node-gpu.js.map b/dist/human.node-gpu.js.map
index 086b1000..2f00f3e8 100644
--- a/dist/human.node-gpu.js.map
+++ b/dist/human.node-gpu.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/tfjs/tf-node-gpu.ts", "../src/human.ts", "../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts"],
-  "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      tf.enableDebugMode();\n      */\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(name: string, raw: any): void {\n  if (!raw || !raw.kernels) return;\n  const maxResults = 5;\n  const time = raw.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n  data[name] = res;\n  log('Human profiler', name, res);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\nconst activateScore = false;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position\n          if (score > config.object.minConfidence) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            let boxRaw = [ // results normalized to range 0..1\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n              cx + (scaleBox / strideSize * boxOffset[2]),\n              cy + (scaleBox / strideSize * boxOffset[3]),\n            ];\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n  const nmsIdx = nms.dataSync();\n  tf.dispose(nms);\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    // @ts-ignore\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    resize.dispose();\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result.clone();\n      profileObject.result.dispose();\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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+  "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      this.tf.enableDebugMode();\n      */\n      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.executeAsync(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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diff --git a/dist/human.node.js b/dist/human.node.js
index 506fcf65..aced0831 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -5,14 +5,14 @@
   author: <https://github.com/vladmandic>'
   */
 
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hn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],an=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],xn=[33,133,362,263,1,78,308],lr=hn.map(e=>I1[e]),dr=an.map(e=>I1[e]),fr=xn.map(e=>I1[e]);var Z1=ce.leftEyeLower0,L1=ce.rightEyeLower0,ue={leftBounds:[Z1[0],Z1[Z1.length-1]],rightBounds:[L1[0],L1[L1.length-1]]},Je={count:468,mouth:13,symmetryLine:[13,ce.midwayBetweenEyes[0]]},Ct={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ke(e,t,n,c){for(let r=0;r<O1.length;r++){let{key:A,indices:_}=O1[r],o=ce[`${n}${A}`];if(!c||c.includes(A))for(let s=0;s<_.length;s++){let i=_[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var V1=class{constructor(t,n,c){var r,A;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=c,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((A=t==null?void 0:t.model)==null?void 0:A.inputs[0].shape[2]),this.irisSize=(c==null?void 0:c.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,c,r){let A=ze({startPoint:n.startPoint,endPoint:n.endPoint}),_=t.map(h=>[A[0]/this.meshSize*(h[0]-this.meshSize/2),A[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=c!==0?Ye(c,[0,0]):Ce,s=c!==0?_.map(h=>[...Bt(h,o),h[2]]):_,i=c!==0?qt(r):Ce,a=[...pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+he(a,i[0]),h[1]+he(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ue.leftBounds[0]][2],c=t[ue.rightBounds[0]][2];return n-c}getEyeBox(t,n,c,r,A=!1){let _=Be(qe(this.calculateLandmarksBoundingBox([t[c],t[r]]),this.irisEnlarge)),o=ze(_),s=X.image.cropAndResize(n,[[_.startPoint[1]/this.meshSize,_.startPoint[0]/this.meshSize,_.endPoint[1]/this.meshSize,_.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return A&&X.ENV.flags.IS_BROWSER&&(s=X.image.flipLeftRight(s)),{box:_,boxSize:o,crop:s}}getEyeCoords(t,n,c,r=!1){let A=[];for(let _=0;_<be.numCoordinates;_++){let o=t[_*3],s=t[_*3+1],i=t[_*3+2];A.push([(r?1-o/this.irisSize:o/this.irisSize)*c[0]+n.startPoint[0],s/this.irisSize*c[1]+n.startPoint[1],i])}return{rawCoords:A,iris:A.slice(be.index)}}getAdjustedIrisCoords(t,n,c){let r=t[ce[`${c}EyeUpper0`][be.upperCenter]][2],A=t[ce[`${c}EyeLower0`][be.lowerCenter]][2],_=(r+A)/2;return n.map((o,s)=>{let i=_;return s===2?i=r:s===4&&(i=A),[o[0],o[1],i]})}async predict(t,n){let c=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let _ of r.boxes)this.storedBoxes.push({startPoint:_.box.startPoint.dataSync(),endPoint:_.box.endPoint.dataSync(),landmarks:_.landmarks,confidence:_.confidence});this.storedBoxes.length>0&&(c=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),c){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let _=0;_<this.storedBoxes.length;_++){let o=Ht({startPoint:this.storedBoxes[_].startPoint,endPoint:this.storedBoxes[_].endPoint},r.scaleFactor),s=qe(o),i=Be(s),a=this.storedBoxes[_].landmarks.arraySync(),h=this.storedBoxes[_].confidence;this.storedBoxes[_]={...i,confidence:h,landmarks:a}}}r&&r.boxes&&r.boxes.forEach(_=>{_.box.startPoint.dispose(),_.box.endPoint.dispose(),_.landmarks.dispose()});let A=X.tidy(()=>this.storedBoxes.map((_,o)=>{let s=_.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=Je.count?Je.symmetryLine:Ct.symmetryLine;a=k1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),P=[p[0]/t.shape[2],p[1]/t.shape[1]],M=X.image.rotateWithOffset(t,a,0,P);h=Ye(-a,p),n.face.mesh.enabled?i=me({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.meshSize,this.meshSize]).div(255):i=me({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.boxSize,this.boxSize]).div(255)}else{h=Ce;let m=t.clone();n.face.mesh.enabled?i=me({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.meshSize,this.meshSize]).div(255):i=me({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,boxConfidence:s,confidence:_.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),f=y.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let z=X.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:m,boxSize:l,crop:p}=this.getEyeBox(z,i,ue.leftBounds[0],ue.leftBounds[1],!0),{box:P,boxSize:M,crop:C}=this.getEyeBox(z,i,ue.rightBounds[0],ue.rightBounds[1]),_e=this.irisModel.predict(X.concat([p,C])).dataSync(),I0=_e.slice(0,be.numCoordinates*3),{rawCoords:vt,iris:Z0}=this.getEyeCoords(I0,m,l,!0),L0=_e.slice(be.numCoordinates*3),{rawCoords:zt,iris:V0}=this.getEyeCoords(L0,P,M),Mt=this.getLeftToRightEyeDepthDifference(z);Math.abs(Mt)<30?(Ke(z,vt,"left",null),Ke(z,zt,"right",null)):Mt<1?Ke(z,vt,"left",["EyeUpper0","EyeLower0"]):Ke(z,zt,"right",["EyeUpper0","EyeLower0"]);let H0=this.getAdjustedIrisCoords(z,Z0,"left"),X0=this.getAdjustedIrisCoords(z,V0,"right");z=z.concat(H0).concat(X0)}let b=this.transformRawCoords(z,_,a,h);_=qe(this.calculateLandmarksBoundingBox(b),1.5);let W=X.tensor2d(b);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=Je.count?Je.symmetryLine:Ct.symmetryLine;a=k1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),P=[p[0]/t.shape[2],p[1]/t.shape[1]],M=X.image.rotateWithOffset(t,a,0,P);h=Ye(-a,p),i=me({startPoint:_.startPoint,endPoint:_.endPoint},M,[this.meshSize,this.meshSize]).div(255)}let E={coords:W,box:_,faceConfidence:f,boxConfidence:s,image:i,rawCoords:z},F=Be(_);return this.storedBoxes[o]={...F,landmarks:b,confidence:_.confidence,faceConfidence:f},E}));return A=A.filter(_=>_!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(_=>_.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=A.length,A}calculateLandmarksBoundingBox(t){let n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A,landmarks:t}}};var F1=class{constructor(t,n,c,r){this.facePipeline=new V1(t,n,c),this.config=r}async estimateFaces(t,n){let c=await this.facePipeline.predict(t,n),r=[];for(let A of c||[]){if(A.isDisposedInternal)continue;let _=A.coords?A.coords.arraySync():[],o=_.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(_&&_.length>0)for(let h of Object.keys(ce))s[h]=ce[h].map(y=>_[y]);let i=A.box?[Math.max(0,A.box.startPoint[0]),Math.max(0,A.box.startPoint[1]),Math.min(t.shape[1],A.box.endPoint[0])-Math.max(0,A.box.startPoint[0]),Math.min(t.shape[2],A.box.endPoint[1])-Math.max(0,A.box.startPoint[1])]:0,a=A.box?[A.box.startPoint[0]/t.shape[2],A.box.startPoint[1]/t.shape[1],(A.box.endPoint[0]-A.box.startPoint[0])/t.shape[2],(A.box.endPoint[1]-A.box.startPoint[1])/t.shape[1]]:[];r.push({confidence:A.faceConfidence||A.boxConfidence||0,boxConfidence:A.boxConfidence,faceConfidence:A.faceConfidence,box:i,boxRaw:a,mesh:_,meshRaw:o,annotations:s,image:A.image?A.image.clone():null}),A.coords&&A.coords.dispose(),A.image&&A.image.dispose()}return r}},le=[null,null,null];async function q1(e){le=await Promise.all([!le[0]&&e.face.enabled?Lt(e):null,!le[1]&&e.face.mesh.enabled?X1.loadGraphModel(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!le[2]&&e.face.iris.enabled?X1.loadGraphModel(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new F1(le[0],le[1],le[2],e);return e.face.mesh.enabled&&e.debug&&u(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&u(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var yn=we;var $1={};Q($1,{PoseNet:()=>et,load:()=>tt});var h0=k(N());var Yt=k(N());function wn(e){let[t,n,c,r]=e;return{offsets:t,heatmap:n,displacementFwd:c,displacementBwd:r}}var B1=class{constructor(t){this.model=t}predict(t){return Yt.tidy(()=>{let c=t.toFloat().div(127.5).sub(1).expandDims(0),A=this.model.predict(c).map(o=>o.squeeze([0])),_=wn(A);return{heatmapScores:_.heatmap.sigmoid(),offsets:_.offsets,displacementFwd:_.displacementFwd,displacementBwd:_.displacementBwd}})}dispose(){this.model.dispose()}};function C1(e){return Math.floor(e/2)}var Y1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(C1(t),t);)this.exchange(t,C1(t)),t=C1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let c=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=c}};function ln(e,t,n,c,r,A){let[_,o]=A.shape,s=!0,i=Math.max(n-r,0),a=Math.min(n+r+1,_);for(let h=i;h<a;++h){let y=Math.max(c-r,0),d=Math.min(c+r+1,o);for(let f=y;f<d;++f)if(A.get(h,f,e)>t){s=!1;break}if(!s)break}return s}function Jt(e,t,n){let[c,r,A]=n.shape,_=new Y1(c*r*A,({score:o})=>o);for(let o=0;o<c;++o)for(let s=0;s<r;++s)for(let i=0;i<A;++i){let a=n.get(o,s,i);a<e||ln(i,a,o,s,t,n)&&_.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return _}var ge=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Te=ge.length,Me=ge.reduce((e,t,n)=>(e[t]=n,e),{}),dn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],jr=dn.map(([e,t])=>[Me[e],Me[t]]),Kt=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function J1(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Te)}}function Ue(e,t,n){let{heatmapY:c,heatmapX:r,id:A}=e,{y:_,x:o}=J1(c,r,A,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+_}}function K1(e,t,n){return e<t?t:e>n?n:e}function Ut(e,t,n,c){let r=n-e,A=c-t;return r*r+A*A}function U1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var J=k(N());function Dt(e,t){let n=t.shape[0],c=new Float32Array(n);for(let r=0;r<n;r++){let A=t.get(r,0),_=t.get(r,1);c[r]=e.get(A,_,r)}return c}function fn(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Te)}}function pn(e,t){let n=[];for(let c=0;c<Te;c++){let r=e.get(c,0).valueOf(),A=e.get(c,1).valueOf(),{x:_,y:o}=fn(r,A,c,t);n.push(o),n.push(_)}return J.tensor2d(n,[Te,2])}function Gt(e,t,n){return J.tidy(()=>e.toTensor().mul(J.scalar(t,"int32")).toFloat().add(pn(e,n)))}function mn(e,t){return J.tidy(()=>{let n=e.div(J.scalar(t,"int32"));return e.sub(n.mul(J.scalar(t,"int32")))})}function Qt(e){let[t,n,c]=e.shape;return J.tidy(()=>{let A=e.reshape([t*n,c]).argMax(0),_=A.div(J.scalar(n,"int32")).expandDims(1),o=mn(A,n).expandDims(1);return J.concat([_,o],1)})}var $t=Kt.map(([e,t])=>[Me[e],Me[t]]),D1=$t.map(([,e])=>e),e0=$t.map(([e])=>e),un=16;function bn(e,t,n){let c=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,c+e)}}function G1(e,t,n,c){return{y:K1(Math.round(e.y/t),0,n-1),x:K1(Math.round(e.x/t),0,c-1)}}function t0(e,t,n,c,r,A,_,o=2){let[s,i]=c.shape,a=G1(t.position,A,s,i),h=bn(e,a,_),d=U1(t.position,h);for(let z=0;z<o;z++){let b=G1(d,A,s,i),W=J1(b.y,b.x,n,r);d=U1({x:b.x*A,y:b.y*A},{x:W.x,y:W.y})}let f=G1(d,A,s,i),x=c.get(f.y,f.x,n);return{position:d,part:ge[n],score:x}}function n0(e,t,n,c,r,A){let _=t.shape[2],o=D1.length,s=new Array(_),{part:i,score:a}=e,h=Ue(i,c,n);s[i.id]={score:a,part:ge[i.id],position:h};for(let y=o-1;y>=0;--y){let d=D1[y],f=e0[y];s[d]&&!s[f]&&(s[f]=t0(y,s[d],f,t,n,c,A))}for(let y=0;y<o;++y){let d=e0[y],f=D1[y];s[d]&&!s[f]&&(s[f]=t0(y,s[d],f,t,n,c,r))}return s}async function r0(e,t,n){let c=0,r=Qt(e),A=await Promise.all([e.buffer(),t.buffer(),r.buffer()]),_=A[0],o=A[1],s=A[2],i=Gt(s,un,o),a=await i.buffer(),y=Array.from(Dt(_,s)).map((f,x)=>(c+=f,{position:{y:a.get(x,0),x:a.get(x,1)},part:ge[x],score:f})),d=y.filter(f=>f.score>n);return r.dispose(),i.dispose(),{keypoints:d,score:c/y.length}}var gn=1,c0=16;function A0(e,t,{x:n,y:c},r){return e.some(({keypoints:A})=>{let _=A[r].position;return Ut(c,n,_.y,_.x)<=t})}function Tn(e,t,n){return n.reduce((r,{position:A,score:_},o)=>(A0(e,t,A,o)||(r+=_),r),0)/n.length}function _0(e,t,n,c,r,A,_){let o=[],s=Jt(_,gn,e),i=r^2;for(;o.length<A&&!s.empty();){let a=s.dequeue(),h=Ue(a.part,c0,t);if(A0(o,i,h,a.part.id))continue;let y=n0(a,e,t,c0,n,c),d=Tn(o,i,y);d>_&&o.push({keypoints:y,score:d})}return o}async function o0(e){return Promise.all(e.map(t=>t.buffer()))}function Pn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:c,part:r,position:A})=>({score:c,part:r,position:{x:Math.trunc(A.x*n),y:Math.trunc(A.y*t)}}))}}function s0(e,[t,n]){let c=e.squeeze(0),r=c.resizeBilinear([t,n]);return c.dispose(),r}function Q1(e,[t,n],[c,r]){return e.map(_=>Pn(_,t/c,n/r))}async function vn(e,t,n,c){return new Promise(async r=>{let A=await o0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),_=A[0],o=A[1],s=A[2],i=A[3],a=await _0(_,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=Q1(a,[e.shape[1],e.shape[2]],[c,c]);r(h)})}async function zn(e,t,n,c){return new Promise(async r=>{let A=await r0(t.heatmapScores,t.offsets,n.body.scoreThreshold),_=Q1([A],[e.shape[1],e.shape[2]],[c,c]);r(_)})}var et=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let c=s0(t,[this.inputSize,this.inputSize]),r=this.baseModel.predict(c,n),A=n.body.maxDetections<2?await zn(t,r,n,this.inputSize):await vn(t,r,n,this.inputSize);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),c.dispose(),A}dispose(){this.baseModel.dispose()}};async function tt(e){let t=await h0.loadGraphModel(e.body.modelPath),n=new B1(t);return e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new et(n)}var _t={};Q(_t,{HandPose:()=>ht,load:()=>it});var ot=k(N());var g=k(N());var i0=k(N());function De(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Re(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function a0(e,t,n){let c=t.shape[1],r=t.shape[2],A=[[e.startPoint[1]/c,e.startPoint[0]/r,e.endPoint[1]/c,e.endPoint[0]/r]];return i0.image.cropAndResize(t,A,[0],n)}function x0(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],c=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(A=>[A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:c,palmLandmarks:r,confidence:e.confidence}}function Ge(e,t=1.5){let n=Re(e),c=De(e),r=[t*c[0]/2,t*c[1]/2],A=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}function Qe(e){let t=Re(e),n=De(e),r=Math.max(...n)/2,A=[t[0]-r,t[1]-r],_=[t[0]+r,t[1]+r];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}var nt=class{constructor(t,n,c){this.model=t,this.anchors=c.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=g.tensor2d(this.anchors),this.inputSize=n,this.inputSizeTensor=g.tensor1d([n,n]),this.doubleInputSizeTensor=g.tensor1d([n*2,n*2])}normalizeBoxes(t){return g.tidy(()=>{let 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n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A}}};var 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u0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],b0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var U;async function xt(e){return U||(U=await ae.loadGraphModel(e.body.modelPath),U.width=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[2].size),U.height=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),U}async function yt(e,t){if(!U||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},c=ae.image.resizeBilinear(e,[U.width,U.height],!1),r=ae.div(c,[255]);c.dispose();let A;if(t.profile){let i=await ae.profile(()=>U.predict(r));A=i.result.find(a=>a.size===195||a.size===155).dataSync(),i.result.forEach(a=>a.dispose()),B("blazepose",i)}else{let i=await U.predict(r);A=i.find(a=>a.size===195||a.size===155).dataSync(),i.forEach(a=>a.dispose())}r.dispose();let _=[],o=A.length===195?u0:b0,s=5;for(let 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sn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],hn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],an=[33,133,362,263,1,78,308],wr=sn.map(e=>H1[e]),lr=hn.map(e=>H1[e]),dr=an.map(e=>H1[e]);var X1=Ae.leftEyeLower0,F1=Ae.rightEyeLower0,be={leftBounds:[X1[0],X1[X1.length-1]],rightBounds:[F1[0],F1[F1.length-1]]},De={count:468,mouth:13,symmetryLine:[13,Ae.midwayBetweenEyes[0]]},Kt={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Ge(e,t,n,c){for(let r=0;r<V1.length;r++){let{key:A,indices:_}=V1[r],o=Ae[`${n}${A}`];if(!c||c.includes(A))for(let s=0;s<_.length;s++){let h=_[s];e[o[s]]=[t[h][0],t[h][1],(t[h][2]+e[o[s]][2])/2]}}}var q1=class{constructor(t,n,c){var r,A;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=c,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((A=t==null?void 0:t.model)==null?void 0:A.inputs[0].shape[2]),this.irisSize=(c==null?void 0:c.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,c,r){let A=Me({startPoint:n.startPoint,endPoint:n.endPoint}),_=t.map(i=>[A[0]/this.meshSize*(i[0]-this.meshSize/2),A[1]/this.meshSize*(i[1]-this.meshSize/2),i[2]]),o=c!==0?Ue(c,[0,0]):Ke,s=c!==0?_.map(i=>[...Jt(i,o),i[2]]):_,h=c!==0?Yt(r):Ke,x=[...pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(i=>[i[0]+he(x,h[0]),i[1]+he(x,h[1]),i[2]])}getLeftToRightEyeDepthDifference(t){let n=t[be.leftBounds[0]][2],c=t[be.rightBounds[0]][2];return n-c}getEyeBox(t,n,c,r,A=!1){let _=Je(Ye(this.calculateLandmarksBoundingBox([t[c],t[r]]),this.irisEnlarge)),o=Me(_),s=X.image.cropAndResize(n,[[_.startPoint[1]/this.meshSize,_.startPoint[0]/this.meshSize,_.endPoint[1]/this.meshSize,_.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return A&&X.ENV.flags.IS_BROWSER&&(s=X.image.flipLeftRight(s)),{box:_,boxSize:o,crop:s}}getEyeCoords(t,n,c,r=!1){let A=[];for(let _=0;_<ge.numCoordinates;_++){let o=t[_*3],s=t[_*3+1],h=t[_*3+2];A.push([(r?1-o/this.irisSize:o/this.irisSize)*c[0]+n.startPoint[0],s/this.irisSize*c[1]+n.startPoint[1],h])}return{rawCoords:A,iris:A.slice(ge.index)}}getAdjustedIrisCoords(t,n,c){let r=t[Ae[`${c}EyeUpper0`][ge.upperCenter]][2],A=t[Ae[`${c}EyeLower0`][ge.lowerCenter]][2],_=(r+A)/2;return n.map((o,s)=>{let h=_;return s===2?h=r:s===4&&(h=A),[o[0],o[1],h]})}async predict(t,n){let c=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let _ of r.boxes)this.storedBoxes.push({startPoint:_.box.startPoint.dataSync(),endPoint:_.box.endPoint.dataSync(),landmarks:_.landmarks,confidence:_.confidence});this.storedBoxes.length>0&&(c=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),c){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let _=0;_<this.storedBoxes.length;_++){let o=qt({startPoint:this.storedBoxes[_].startPoint,endPoint:this.storedBoxes[_].endPoint},r.scaleFactor),s=Ye(o),h=Je(s),x=this.storedBoxes[_].landmarks.arraySync(),i=this.storedBoxes[_].confidence;this.storedBoxes[_]={...h,confidence:i,landmarks:x}}}r&&r.boxes&&r.boxes.forEach(_=>{_.box.startPoint.dispose(),_.box.endPoint.dispose(),_.landmarks.dispose()});let A=X.tidy(()=>this.storedBoxes.map((_,o)=>{let s=_.confidence,h,x=0,i;if(n.face.detector.rotation&&n.face.mesh.enabled&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=De.count?De.symmetryLine:Kt.symmetryLine;x=L1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),v=[p[0]/t.shape[2],p[1]/t.shape[1]],E=X.image.rotateWithOffset(t,x,0,v);i=Ue(-x,p),n.face.mesh.enabled?h=ue({startPoint:_.startPoint,endPoint:_.endPoint},E,[this.meshSize,this.meshSize]).div(255):h=ue({startPoint:_.startPoint,endPoint:_.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{i=Ke;let m=t.clone();n.face.mesh.enabled?h=ue({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.meshSize,this.meshSize]).div(255):h=ue({startPoint:_.startPoint,endPoint:_.endPoint},m,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:_,faceConfidence:null,boxConfidence:s,confidence:_.confidence,image:h};let[,y,f]=this.meshDetector.predict(h),d=y.dataSync()[0];if(d<n.face.detector.minConfidence)return null;let P=X.reshape(f,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:m,boxSize:l,crop:p}=this.getEyeBox(P,h,be.leftBounds[0],be.leftBounds[1],!0),{box:v,boxSize:E,crop:C}=this.getEyeBox(P,h,be.rightBounds[0],be.rightBounds[1]),te=this.irisModel.predict(X.concat([p,C])).dataSync(),f1=te.slice(0,ge.numCoordinates*3),{rawCoords:Ne,iris:ye}=this.getEyeCoords(f1,m,l,!0),Z0=te.slice(ge.numCoordinates*3),{rawCoords:Et,iris:L0}=this.getEyeCoords(Z0,v,E),St=this.getLeftToRightEyeDepthDifference(P);Math.abs(St)<30?(Ge(P,Ne,"left",null),Ge(P,Et,"right",null)):St<1?Ge(P,Ne,"left",["EyeUpper0","EyeLower0"]):Ge(P,Et,"right",["EyeUpper0","EyeLower0"]);let V0=this.getAdjustedIrisCoords(P,ye,"left"),H0=this.getAdjustedIrisCoords(P,L0,"right");P=P.concat(V0).concat(H0)}let b=this.transformRawCoords(P,_,x,i);_=Ye(this.calculateLandmarksBoundingBox(b),1.5);let M=X.tensor2d(b);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&X.ENV.flags.IS_BROWSER){let[m,l]=_.landmarks.length>=De.count?De.symmetryLine:Kt.symmetryLine;x=L1(_.landmarks[m],_.landmarks[l]);let p=pe({startPoint:_.startPoint,endPoint:_.endPoint}),v=[p[0]/t.shape[2],p[1]/t.shape[1]],E=X.image.rotateWithOffset(t,x,0,v);i=Ue(-x,p),h=ue({startPoint:_.startPoint,endPoint:_.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let W={coords:M,box:_,faceConfidence:d,boxConfidence:s,image:h,rawCoords:P},Y=Je(_);return this.storedBoxes[o]={...Y,landmarks:b,confidence:_.confidence,faceConfidence:d},W}));return A=A.filter(_=>_!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(_=>_.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=A.length,A}calculateLandmarksBoundingBox(t){let n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A,landmarks:t}}};var Y1=class{constructor(t,n,c,r){this.facePipeline=new q1(t,n,c),this.config=r}async estimateFaces(t,n){let c=await this.facePipeline.predict(t,n),r=[];for(let A of c||[]){if(A.isDisposedInternal)continue;let _=A.coords?A.coords.arraySync():[],o=_.map(i=>[i[0]/t.shape[2],i[1]/t.shape[1],i[2]/this.facePipeline.meshSize]),s={};if(_&&_.length>0)for(let i of Object.keys(Ae))s[i]=Ae[i].map(y=>_[y]);let h=A.box?[Math.max(0,A.box.startPoint[0]),Math.max(0,A.box.startPoint[1]),Math.min(t.shape[1],A.box.endPoint[0])-Math.max(0,A.box.startPoint[0]),Math.min(t.shape[2],A.box.endPoint[1])-Math.max(0,A.box.startPoint[1])]:0,x=A.box?[A.box.startPoint[0]/t.shape[2],A.box.startPoint[1]/t.shape[1],(A.box.endPoint[0]-A.box.startPoint[0])/t.shape[2],(A.box.endPoint[1]-A.box.startPoint[1])/t.shape[1]]:[];r.push({confidence:A.faceConfidence||A.boxConfidence||0,boxConfidence:A.boxConfidence,faceConfidence:A.faceConfidence,box:h,boxRaw:x,mesh:_,meshRaw:o,annotations:s,image:A.image?A.image.clone():null}),A.coords&&A.coords.dispose(),A.image&&A.image.dispose()}return r}},de=[null,null,null];async function J1(e){de=await Promise.all([!de[0]&&e.face.enabled?Xt(e):null,!de[1]&&e.face.mesh.enabled?B1.loadGraphModel(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!de[2]&&e.face.iris.enabled?B1.loadGraphModel(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new Y1(de[0],de[1],de[2],e);return e.face.mesh.enabled&&e.debug&&u(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&u(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}var xn=le;var rt={};Q(rt,{PoseNet:()=>ct,load:()=>At});var x0=O(N());var Ut=O(N());function yn(e){let[t,n,c,r]=e;return{offsets:t,heatmap:n,displacementFwd:c,displacementBwd:r}}var K1=class{constructor(t){this.model=t}predict(t){return Ut.tidy(()=>{let c=t.toFloat().div(127.5).sub(1).expandDims(0),A=this.model.predict(c).map(o=>o.squeeze([0])),_=yn(A);return{heatmapScores:_.heatmap.sigmoid(),offsets:_.offsets,displacementFwd:_.displacementFwd,displacementBwd:_.displacementBwd}})}dispose(){this.model.dispose()}};function U1(e){return Math.floor(e/2)}var D1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(U1(t),t);)this.exchange(t,U1(t)),t=U1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let c=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=c}};function wn(e,t,n,c,r,A){let[_,o]=A.shape,s=!0,h=Math.max(n-r,0),x=Math.min(n+r+1,_);for(let i=h;i<x;++i){let y=Math.max(c-r,0),f=Math.min(c+r+1,o);for(let d=y;d<f;++d)if(A.get(i,d,e)>t){s=!1;break}if(!s)break}return s}function Dt(e,t,n){let[c,r,A]=n.shape,_=new D1(c*r*A,({score:o})=>o);for(let o=0;o<c;++o)for(let s=0;s<r;++s)for(let h=0;h<A;++h){let x=n.get(o,s,h);x<e||wn(h,x,o,s,t,n)&&_.enqueue({score:x,part:{heatmapY:o,heatmapX:s,id:h}})}return _}var Te=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Pe=Te.length,Re=Te.reduce((e,t,n)=>(e[t]=n,e),{}),ln=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Sr=ln.map(([e,t])=>[Re[e],Re[t]]),Gt=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function G1(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Pe)}}function Qe(e,t,n){let{heatmapY:c,heatmapX:r,id:A}=e,{y:_,x:o}=G1(c,r,A,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+_}}function Q1(e,t,n){return e<t?t:e>n?n:e}function Qt(e,t,n,c){let r=n-e,A=c-t;return r*r+A*A}function $1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var J=O(N());function $t(e,t){let n=t.shape[0],c=new Float32Array(n);for(let r=0;r<n;r++){let A=t.get(r,0),_=t.get(r,1);c[r]=e.get(A,_,r)}return c}function dn(e,t,n,c){return{y:c.get(e,t,n),x:c.get(e,t,n+Pe)}}function fn(e,t){let n=[];for(let c=0;c<Pe;c++){let r=e.get(c,0).valueOf(),A=e.get(c,1).valueOf(),{x:_,y:o}=dn(r,A,c,t);n.push(o),n.push(_)}return J.tensor2d(n,[Pe,2])}function e0(e,t,n){return J.tidy(()=>e.toTensor().mul(J.scalar(t,"int32")).toFloat().add(fn(e,n)))}function mn(e,t){return J.tidy(()=>{let n=e.div(J.scalar(t,"int32"));return e.sub(n.mul(J.scalar(t,"int32")))})}function t0(e){let[t,n,c]=e.shape;return J.tidy(()=>{let A=e.reshape([t*n,c]).argMax(0),_=A.div(J.scalar(n,"int32")).expandDims(1),o=mn(A,n).expandDims(1);return J.concat([_,o],1)})}var n0=Gt.map(([e,t])=>[Re[e],Re[t]]),et=n0.map(([,e])=>e),r0=n0.map(([e])=>e),pn=16;function un(e,t,n){let c=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,c+e)}}function tt(e,t,n,c){return{y:Q1(Math.round(e.y/t),0,n-1),x:Q1(Math.round(e.x/t),0,c-1)}}function c0(e,t,n,c,r,A,_,o=2){let[s,h]=c.shape,x=tt(t.position,A,s,h),i=un(e,x,_),f=$1(t.position,i);for(let P=0;P<o;P++){let b=tt(f,A,s,h),M=G1(b.y,b.x,n,r);f=$1({x:b.x*A,y:b.y*A},{x:M.x,y:M.y})}let d=tt(f,A,s,h),a=c.get(d.y,d.x,n);return{position:f,part:Te[n],score:a}}function A0(e,t,n,c,r,A){let _=t.shape[2],o=et.length,s=new Array(_),{part:h,score:x}=e,i=Qe(h,c,n);s[h.id]={score:x,part:Te[h.id],position:i};for(let y=o-1;y>=0;--y){let f=et[y],d=r0[y];s[f]&&!s[d]&&(s[d]=c0(y,s[f],d,t,n,c,A))}for(let y=0;y<o;++y){let f=r0[y],d=et[y];s[f]&&!s[d]&&(s[d]=c0(y,s[f],d,t,n,c,r))}return s}async function _0(e,t,n){let c=0,r=t0(e),A=await Promise.all([e.buffer(),t.buffer(),r.buffer()]),_=A[0],o=A[1],s=A[2],h=e0(s,pn,o),x=await h.buffer(),y=Array.from($t(_,s)).map((d,a)=>(c+=d,{position:{y:x.get(a,0),x:x.get(a,1)},part:Te[a],score:d})),f=y.filter(d=>d.score>n);return r.dispose(),h.dispose(),{keypoints:f,score:c/y.length}}var bn=1,o0=16;function s0(e,t,{x:n,y:c},r){return e.some(({keypoints:A})=>{let _=A[r].position;return Qt(c,n,_.y,_.x)<=t})}function gn(e,t,n){return n.reduce((r,{position:A,score:_},o)=>(s0(e,t,A,o)||(r+=_),r),0)/n.length}function h0(e,t,n,c,r,A,_){let o=[],s=Dt(_,bn,e),h=r^2;for(;o.length<A&&!s.empty();){let x=s.dequeue(),i=Qe(x.part,o0,t);if(s0(o,h,i,x.part.id))continue;let y=A0(x,e,t,o0,n,c),f=gn(o,h,y);f>_&&o.push({keypoints:y,score:f})}return o}async function i0(e){return Promise.all(e.map(t=>t.buffer()))}function Tn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:c,part:r,position:A})=>({score:c,part:r,position:{x:Math.trunc(A.x*n),y:Math.trunc(A.y*t)}}))}}function a0(e,[t,n]){let c=e.squeeze(0),r=c.resizeBilinear([t,n]);return c.dispose(),r}function nt(e,[t,n],[c,r]){return e.map(_=>Tn(_,t/c,n/r))}async function Pn(e,t,n,c){return new Promise(async r=>{let A=await i0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),_=A[0],o=A[1],s=A[2],h=A[3],x=await h0(_,o,s,h,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),i=nt(x,[e.shape[1],e.shape[2]],[c,c]);r(i)})}async function vn(e,t,n,c){return new Promise(async r=>{let A=await _0(t.heatmapScores,t.offsets,n.body.scoreThreshold),_=nt([A],[e.shape[1],e.shape[2]],[c,c]);r(_)})}var ct=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let c=a0(t,[this.inputSize,this.inputSize]),r=this.baseModel.predict(c,n),A=n.body.maxDetections<2?await vn(t,r,n,this.inputSize):await Pn(t,r,n,this.inputSize);return r.heatmapScores.dispose(),r.offsets.dispose(),r.displacementFwd.dispose(),r.displacementBwd.dispose(),c.dispose(),A}dispose(){this.baseModel.dispose()}};async function At(e){let t=await x0.loadGraphModel(e.body.modelPath),n=new K1(t);return e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new ct(n)}var it={};Q(it,{HandPose:()=>yt,load:()=>wt});var at=O(N());var g=O(N());var y0=O(N());function $e(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ee(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function w0(e,t,n){let c=t.shape[1],r=t.shape[2],A=[[e.startPoint[1]/c,e.startPoint[0]/r,e.endPoint[1]/c,e.endPoint[0]/r]];return y0.image.cropAndResize(t,A,[0],n)}function l0(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],c=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(A=>[A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:c,palmLandmarks:r,confidence:e.confidence}}function e1(e,t=1.5){let n=Ee(e),c=$e(e),r=[t*c[0]/2,t*c[1]/2],A=[n[0]-r[0],n[1]-r[1]],_=[n[0]+r[0],n[1]+r[1]];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}function t1(e){let t=Ee(e),n=$e(e),r=Math.max(...n)/2,A=[t[0]-r,t[1]-r],_=[t[0]+r,t[1]+r];return{startPoint:A,endPoint:_,palmLandmarks:e.palmLandmarks}}var _t=class{constructor(t,n,c){this.model=t,this.anchors=c.map(r=>[r.x_center,r.y_center]),this.anchorsTensor=g.tensor2d(this.anchors),this.inputSize=n,this.inputSizeTensor=g.tensor1d([n,n]),this.doubleInputSizeTensor=g.tensor1d([n*2,n*2])}normalizeBoxes(t){return g.tidy(()=>{let 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n=t.map(_=>_[0]),c=t.map(_=>_[1]),r=[Math.min(...n),Math.min(...c)],A=[Math.max(...n),Math.max(...c)];return{startPoint:r,endPoint:A}}};var 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T0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],P0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var U;async function dt(e){return U||(U=await ae.loadGraphModel(e.body.modelPath),U.width=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[2].size),U.height=parseInt(U.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&u(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),U}async function ft(e,t){if(!U||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},c=ae.image.resizeBilinear(e,[U.width,U.height],!1),r=ae.div(c,[255]);c.dispose();let A;if(t.profile){let h=await ae.profile(()=>U.predict(r));A=h.result.find(x=>x.size===195||x.size===155).dataSync(),h.result.forEach(x=>x.dispose()),q("blazepose",h)}else{let h=await U.predict(r);A=h.find(x=>x.size===195||x.size===155).dataSync(),h.forEach(x=>x.dispose())}r.dispose();let _=[],o=A.length===195?T0:P0,s=5;for(let 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+`),i.pixelate=function(m){let l=m/o,p=m/s,v=Y(i.pixelate.SHADER);a.uniform2f(v.uniform.size,l,p),W()},i.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
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h;if(V.data){let i=[V.height,V.width,3];h=ee.tensor3d(V.data,i,"int32")}else if(t.backend==="webgl"||V instanceof ImageData)h=ee.browser.fromPixels(V);else{let i=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(_,o):document.createElement("canvas");i.width=_,i.height=o;let y=i.getContext("2d");y==null||y.drawImage(V,0,0);let f=y==null?void 0:y.getImageData(0,0,_,o);h=ee.browser.fromPixels(f)}let x=h.toFloat();n=x.expandDims(0),h.dispose(),x.dispose()}let c=t.filter.return?V:null;return{tensor:n,canvas:c}}var vt={};Q(vt,{all:()=>In,body:()=>W0,canvas:()=>kn,drawOptions:()=>w,face:()=>j0,gesture:()=>S0,hand:()=>N0,object:()=>O0});var j={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},description:{enabled:!0,modelPath:"../models/faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"../models/emotion.json"},age:{enabled:!1,modelPath:"../models/age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}},object:{enabled:!1,modelPath:"../models/nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var w={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function h1(e,t,n,c=null){e.fillStyle=w.useDepth&&c?`rgba(${127.5+2*(c||0)}, ${127.5-2*(c||0)}, 255, 0.3)`:w.color,e.beginPath(),e.arc(t,n,w.pointSize,0,2*Math.PI),e.fill()}function ve(e,t,n,c,r){if(e.beginPath(),w.useCurves){let A=(t+t+c)/2,_=(n+n+r)/2;e.ellipse(A,_,c/2,r/2,0,0,2*Math.PI)}else e.lineWidth=w.lineWidth,e.moveTo(t+w.roundRect,n),e.lineTo(t+c-w.roundRect,n),e.quadraticCurveTo(t+c,n,t+c,n+w.roundRect),e.lineTo(t+c,n+r-w.roundRect),e.quadraticCurveTo(t+c,n+r,t+c-w.roundRect,n+r),e.lineTo(t+w.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-w.roundRect),e.lineTo(t,n+w.roundRect),e.quadraticCurveTo(t,n,t+w.roundRect,n),e.closePath();e.stroke()}function zt(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=w.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:w.color,e.fillStyle=w.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:w.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),w.fillPolygons&&(e.closePath(),e.fill())}}function i1(e,t=[]){if(!(t===void 0||t.length===0)){if(!w.useCurves||t.length<=2){zt(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let c=(t[n][0]+t[n+1][0])/2,r=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],c,r)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),w.fillPolygons&&(e.closePath(),e.fill())}}async function S0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=w.font,n.fillStyle=w.color;let c=1;for(let r=0;r<t.length;r++){let A=[],_=[];if([A,_]=Object.entries(t[r]),_.length>1&&_[1].length>0){let o=A[1]>0?`#${A[1]}`:"",s=`${A[0]} ${o}: ${_[1]}`;w.shadowColor&&w.shadowColor!==""&&(n.fillStyle=w.shadowColor,n.fillText(s,8,2+c*w.lineHeight)),n.fillStyle=w.labelColor,n.fillText(s,6,0+c*w.lineHeight),c+=1}}}async function j0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let c of t){n.font=w.font,n.strokeStyle=w.color,n.fillStyle=w.color,w.drawBoxes&&(w.useRawBoxes?ve(n,e.width*c.boxRaw[0],e.height*c.boxRaw[1],e.width*c.boxRaw[2],e.height*c.boxRaw[3]):ve(n,c.box[0],c.box[1],c.box[2],c.box[3]));let r=[];if(r.push(`face confidence: ${Math.trunc(100*c.confidence)}%`),c.genderConfidence&&r.push(`${c.gender||""} ${Math.trunc(100*c.genderConfidence)}% confident`),c.age&&r.push(`age: ${c.age||""}`),c.iris&&r.push(`iris distance: ${c.iris}`),c.emotion&&c.emotion.length>0){let A=c.emotion.map(_=>`${Math.trunc(100*_.score)}% ${_.emotion}`);r.push(A.join(" "))}c.angle&&c.angle.roll&&r.push(`roll: ${Math.trunc(100*c.angle.roll)/100} yaw:${Math.trunc(100*c.angle.yaw)/100} pitch:${Math.trunc(100*c.angle.pitch)/100}`),r.length===0&&r.push("face"),n.fillStyle=w.color;for(let A=r.length-1;A>=0;A--){let _=Math.max(c.box[0],0),o=A*w.lineHeight+c.box[1];w.shadowColor&&w.shadowColor!==""&&(n.fillStyle=w.shadowColor,n.fillText(r[A],_+5,o+16)),n.fillStyle=w.labelColor,n.fillText(r[A],_+4,o+15)}if(n.lineWidth=1,c.mesh&&c.mesh.length>0){if(w.drawPoints)for(let A of c.mesh)h1(n,A[0],A[1],A[2]);if(w.drawPolygons){n.lineWidth=1;for(let A=0;A<le.length/3;A++){let _=[le[A*3+0],le[A*3+1],le[A*3+2]].map(o=>c.mesh[o]);zt(n,_)}if(c.annotations&&c.annotations.leftEyeIris){n.strokeStyle=w.useDepth?"rgba(255, 200, 255, 0.3)":w.color,n.beginPath();let A=Math.abs(c.annotations.leftEyeIris[3][0]-c.annotations.leftEyeIris[1][0])/2,_=Math.abs(c.annotations.leftEyeIris[4][1]-c.annotations.leftEyeIris[2][1])/2;n.ellipse(c.annotations.leftEyeIris[0][0],c.annotations.leftEyeIris[0][1],A,_,0,0,2*Math.PI),n.stroke(),w.fillPolygons&&(n.fillStyle=w.useDepth?"rgba(255, 255, 200, 0.3)":w.color,n.fill())}if(c.annotations&&c.annotations.rightEyeIris){n.strokeStyle=w.useDepth?"rgba(255, 200, 255, 0.3)":w.color,n.beginPath();let A=Math.abs(c.annotations.rightEyeIris[3][0]-c.annotations.rightEyeIris[1][0])/2,_=Math.abs(c.annotations.rightEyeIris[4][1]-c.annotations.rightEyeIris[2][1])/2;n.ellipse(c.annotations.rightEyeIris[0][0],c.annotations.rightEyeIris[0][1],A,_,0,0,2*Math.PI),n.stroke(),w.fillPolygons&&(n.fillStyle=w.useDepth?"rgba(255, 255, 200, 0.3)":w.color,n.fill())}}}}}var xe=[];async function W0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let c=0;c<t.length;c++){if(!xe[c]&&w.bufferedOutput&&(xe[c]={...t[c]}),n.strokeStyle=w.color,n.lineWidth=w.lineWidth,w.drawPoints)for(let r=0;r<t[c].keypoints.length;r++)n.fillStyle=w.useDepth&&t[c].keypoints[r].position.z?`rgba(${127.5+2*t[c].keypoints[r].position.z}, ${127.5-2*t[c].keypoints[r].position.z}, 255, 0.5)`:w.color,w.bufferedOutput?(xe[c].keypoints[r][0]=(xe[c].keypoints[r][0]+t[c].keypoints[r].position.x)/2,xe[c].keypoints[r][1]=(xe[c].keypoints[r][1]+t[c].keypoints[r].position.y)/2,h1(n,xe[c].keypoints[r][0],xe[c].keypoints[r][1])):h1(n,t[c].keypoints[r].position.x,t[c].keypoints[r].position.y);if(w.drawLabels&&(n.font=w.font,t[c].keypoints))for(let r of t[c].keypoints)n.fillStyle=w.useDepth&&r.position.z?`rgba(${127.5+2*r.position.z}, ${127.5-2*r.position.z}, 255, 0.5)`:w.color,n.fillText(`${r.part}`,r.position.x+4,r.position.y+4);if(w.drawPolygons&&t[c].keypoints){let r,A=[];A.length=0,r=t[c].keypoints.find(_=>_.part==="leftShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),A.length===5&&zt(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="leftHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftKnee"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftAnkle"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftHeel"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftFoot"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="rightHip"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightKnee"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightAnkle"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightHeel"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightFoot"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="leftShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftElbow"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftWrist"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="leftPalm"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A),A.length=0,r=t[c].keypoints.find(_=>_.part==="rightShoulder"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightElbow"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightWrist"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),r=t[c].keypoints.find(_=>_.part==="rightPalm"),r&&r.score>j.body.scoreThreshold&&A.push([r.position.x,r.position.y]),i1(n,A)}}}}async function N0(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=w.font;for(let c of t){if(w.drawBoxes&&(n.strokeStyle=w.color,n.fillStyle=w.color,w.useRawBoxes?ve(n,e.width*c.boxRaw[0],e.height*c.boxRaw[1],e.width*c.boxRaw[2],e.height*c.boxRaw[3]):ve(n,c.box[0],c.box[1],c.box[2],c.box[3]),w.drawLabels&&(w.shadowColor&&w.shadowColor!==""&&(n.fillStyle=w.shadowColor,n.fillText("hand",c.box[0]+3,1+c.box[1]+w.lineHeight,c.box[2])),n.fillStyle=w.labelColor,n.fillText("hand",c.box[0]+2,0+c.box[1]+w.lineHeight,c.box[2])),n.stroke()),w.drawPoints&&c.landmarks&&c.landmarks.length>0)for(let r of c.landmarks)n.fillStyle=w.useDepth?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.5)`:w.color,h1(n,r[0],r[1]);if(w.drawPolygons){let r=A=>{if(!!A)for(let _=0;_<A.length;_++)n.lineWidth=w.lineWidth,n.beginPath(),n.strokeStyle=w.useDepth?`rgba(${127.5+2*A[_][2]}, ${127.5-2*A[_][2]}, 255, 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backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&u("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let c=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),r=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&u(`wasm execution: ${c?"SIMD":"no SIMD"} ${r?"multithreaded":"singlethreaded"}`),c||u("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&Nt();try{await this.tf.setBackend(this.config.backend)}catch(c){u("error: cannot set backend:",this.config.backend,c)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let c=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${c.getParameter(c.VERSION)} renderer:${c.getParameter(c.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-n)}});i1.set(this,async()=>{let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(_=>_.blob()),n,c;switch(this.config.warmup){case"face":n=await t(o1);break;case"full":n=await t(s1);break;default:n=null}if(n){let r=await createImageBitmap(n);c=await this.detect(r,this.config),r.close()}return c});a1.set(this,async()=>new Promise(t=>{let n,c=0;switch(this.config.warmup){case"face":c=256,n="data:image/jpeg;base64,"+o1;break;case"full":case"body":c=1200,n="data:image/jpeg;base64,"+s1;break;default:n=null}let r=new Image;r.onload=async()=>{let A=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(c,c):document.createElement("canvas");A.width=r.naturalWidth,A.height=r.naturalHeight;let _=A.getContext("2d");_==null||_.drawImage(r,0,0);let o=await this.detect(A,this.config);t(o)},n?r.src=n:t(null)}));x1.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?t(o1):t(s1),c=Tt.node.decodeJpeg(n),r=c.expandDims(0);this.tf.dispose(c);let A=await this.detect(r,this.config);return this.tf.dispose(r),A});this.tf=Tt,this.draw=bt,this.version=k0,this.config=fe(j,t),this.state="idle",ye(this,ve,0),ye(this,Ee,!1),ye(this,Se,!1),ye(this,de,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>ut(n,this.config),this.classes={facemesh:H1,age:d1,gender:p1,emotion:T1,faceres:E1,body:this.config.body.modelPath.includes("posenet")?$1:at,hand:_t,nanodet:dt},this.sysinfo=St()}profileData(){return this.config.profile?l1:{}}similarity(t,n){return this.config.face.description.enabled?j1(t,n):this.config.face.embedding.enabled?kt(t,n):0}enhance(t){return W1(t)}match(t,n,c=0){return Ot(t,n,c)}async load(t={}){this.state="load";let n=v();t&&(this.config=fe(this.config,t)),K(this,de)&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version_core}`),this.config.debug&&u("platform:",this.sysinfo.platform),this.config.debug&&u("agent:",this.sysinfo.agent),await K(this,je).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?q1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?f1(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?g1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?z1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?M1(this.config):null),this.models.handpose||(this.config.hand.enabled?it(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?tt(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?xt(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?wt(this.config):null),this.models.nanodet||(this.config.object.enabled?pt(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?S1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await q1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await f1(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await g1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await z1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await M1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await it(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await tt(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await xt(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await wt(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await pt(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await S1(this.config))),K(this,de)&&(this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ye(this,de,!1));let c=Math.trunc(v()-n);c>(this.perf.load||0)&&(this.perf.load=c)}async detect(t,n={}){return new Promise(async c=>{var x,z,b,W;this.state="config";let r;this.config=fe(this.config,n),this.state="check";let A=K(this,h1).call(this,t);A&&(u(A,t),c({error:A}));let _=v();await K(this,je).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=v();let o=ut(t,this.config);if(!o||!o.tensor){u("could not convert input to tensor"),c({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-r),this.analyze("Get Image:");let s,i,a,h,y;this.config.async?(a=this.config.face.enabled?N1(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=v(),a=this.config.face.enabled?await N1(this,o.tensor):[],y=Math.trunc(v()-r),y>0&&(this.perf.face=y)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?(x=this.models.posenet)==null?void 0:x.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?yt(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?lt(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=v(),this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?await((z=this.models.posenet)==null?void 0:z.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?await yt(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?await lt(o.tensor,this.config):[]),y=Math.trunc(v()-r),y>0&&(this.perf.body=y)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(i=this.config.hand.enabled?(b=this.models.handpose)==null?void 0:b.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=v(),i=this.config.hand.enabled?await((W=this.models.handpose)==null?void 0:W.estimateHands(o.tensor,this.config)):[],y=Math.trunc(v()-r),y>0&&(this.perf.hand=y)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(h=this.config.object.enabled?mt(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=v(),h=this.config.object.enabled?await mt(o.tensor,this.config):[],y=Math.trunc(v()-r),y>0&&(this.perf.object=y)),this.analyze("End Object:"),this.config.async&&([a,s,i,h]=await Promise.all([a,s,i,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let d=[];this.config.gesture.enabled&&(r=v(),d=[...v0(a),...P0(s),...M0(i),...z0(a)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-r)),this.perf.total=Math.trunc(v()-_),this.state="idle";let f={face:a,body:s,hand:i,gesture:d,object:h,performance:this.perf,canvas:o.canvas};c(f)})}async warmup(t={}){let n=v();t&&(this.config=fe(this.config,t));let c=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await K(this,i1).call(this):typeof Image!="undefined"?r=await K(this,a1).call(this):r=await K(this,x1).call(this),this.config.videoOptimized=c;let A=v();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(A-n),"ms",r),r}};ve=new WeakMap,Ee=new WeakMap,Se=new WeakMap,de=new WeakMap,h1=new WeakMap,je=new WeakMap,i1=new WeakMap,a1=new WeakMap,x1=new WeakMap;0&&(module.exports={Human});
+2Q==`;var k0="1.2.5";var ze,Se,je,fe,y1,We,w1,l1,d1,I0=class{constructor(t={}){ze.set(this,void 0);Se.set(this,void 0);je.set(this,void 0);fe.set(this,void 0);this.analyze=(...t)=>{if(!K(this,Se))return;let n=this.tf.engine().state.numTensors,c=K(this,ze);we(this,ze,n);let r=n-c;r!==0&&u(...t,r)};y1.set(this,t=>{if(!K(this,je))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Mt.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});We.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=z();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="wasm")&&(this.config.backend="tensorflow"),this.config.debug&&u("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&u("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let c=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),r=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&u(`wasm execution: ${c?"SIMD":"no SIMD"} ${r?"multithreaded":"singlethreaded"}`),c||u("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&It();try{await this.tf.setBackend(this.config.backend)}catch(c){u("error: cannot set backend:",this.config.backend,c)}}if(this.tf.enableProdMode(),this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.tf.getBackend()==="webgl"){this.config.deallocate&&(u("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let c=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&u(`gl version:${c.getParameter(c.VERSION)} renderer:${c.getParameter(c.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(z()-n)}});w1.set(this,async()=>{let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(_=>_.blob()),n,c;switch(this.config.warmup){case"face":n=await t(a1);break;case"full":n=await t(x1);break;default:n=null}if(n){let r=await createImageBitmap(n);c=await this.detect(r,this.config),r.close()}return c});l1.set(this,async()=>new Promise(t=>{let n,c=0;switch(this.config.warmup){case"face":c=256,n="data:image/jpeg;base64,"+a1;break;case"full":case"body":c=1200,n="data:image/jpeg;base64,"+x1;break;default:n=null}let r=new Image;r.onload=async()=>{let A=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(c,c):document.createElement("canvas");A.width=r.naturalWidth,A.height=r.naturalHeight;let _=A.getContext("2d");_==null||_.drawImage(r,0,0);let o=await this.detect(A,this.config);t(o)},n?r.src=n:t(null)}));d1.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n=this.config.warmup==="face"?t(a1):t(x1),c=Mt.node.decodeJpeg(n),r=c.expandDims(0);this.tf.dispose(c);let A=await this.detect(r,this.config);return this.tf.dispose(r),A});this.tf=Mt,this.draw=vt,this.version=k0,this.config=me(j,t),this.state="idle",we(this,ze,0),we(this,Se,!1),we(this,je,!1),we(this,fe,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>Pt(n,this.config),this.classes={facemesh:C1,age:u1,gender:g1,emotion:M1,faceres:N1,body:this.config.body.modelPath.includes("posenet")?rt:lt,hand:it,nanodet:ut},this.sysinfo=Nt()}profileData(){return this.config.profile?ke:{}}similarity(t,n){return this.config.face.description.enabled?k1(t,n):this.config.face.embedding.enabled?Zt(t,n):0}enhance(t){return I1(t)}match(t,n,c=0){return Lt(t,n,c)}async load(t={}){this.state="load";let n=z();t&&(this.config=me(this.config,t)),K(this,fe)&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version_core}`),this.config.debug&&u("platform:",this.sysinfo.platform),this.config.debug&&u("agent:",this.sysinfo.agent),await K(this,We).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?J1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?b1(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?z1(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?S1(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?j1(this.config):null),this.models.handpose||(this.config.hand.enabled?wt(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?At(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?dt(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?mt(this.config):null),this.models.nanodet||(this.config.object.enabled?gt(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?O1(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await J1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await b1(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await z1(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await S1(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await j1(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await wt(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await At(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await dt(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await mt(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await gt(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await O1(this.config))),K(this,fe)&&(this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),we(this,fe,!1));let c=Math.trunc(z()-n);c>(this.perf.load||0)&&(this.perf.load=c)}async detect(t,n={}){return new Promise(async c=>{var a,P,b,M;this.state="config";let r;this.config=me(this.config,n),this.state="check";let A=K(this,y1).call(this,t);A&&(u(A,t),c({error:A}));let _=z();await K(this,We).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),r=z();let o=Pt(t,this.config);if(!o||!o.tensor){u("could not convert input to tensor"),c({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(z()-r),this.analyze("Get Image:");let s,h,x,i,y;this.config.async?(x=this.config.face.enabled?Z1(this,o.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=z(),x=this.config.face.enabled?await Z1(this,o.tensor):[],y=Math.trunc(z()-r),y>0&&(this.perf.face=y)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?(a=this.models.posenet)==null?void 0:a.estimatePoses(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?ft(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?pt(o.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=z(),this.config.body.modelPath.includes("posenet")?s=this.config.body.enabled?await((P=this.models.posenet)==null?void 0:P.estimatePoses(o.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?s=this.config.body.enabled?await ft(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(s=this.config.body.enabled?await pt(o.tensor,this.config):[]),y=Math.trunc(z()-r),y>0&&(this.perf.body=y)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?(b=this.models.handpose)==null?void 0:b.estimateHands(o.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=z(),h=this.config.hand.enabled?await((M=this.models.handpose)==null?void 0:M.estimateHands(o.tensor,this.config)):[],y=Math.trunc(z()-r),y>0&&(this.perf.hand=y)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(i=this.config.object.enabled?Tt(o.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=z(),i=this.config.object.enabled?await Tt(o.tensor,this.config):[],y=Math.trunc(z()-r),y>0&&(this.perf.object=y)),this.analyze("End Object:"),this.config.async&&([x,s,h,i]=await Promise.all([x,s,h,i])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(r=z(),f=[...z0(x),...v0(s),...R0(h),...M0(x)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(z()-r)),this.perf.total=Math.trunc(z()-_),this.state="idle";let d={face:x,body:s,hand:h,gesture:f,object:i,performance:this.perf,canvas:o.canvas};c(d)})}async warmup(t={}){let n=z();t&&(this.config=me(this.config,t));let c=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await K(this,w1).call(this):typeof Image!="undefined"?r=await K(this,l1).call(this):r=await K(this,d1).call(this),this.config.videoOptimized=c;let A=z();return this.config.debug&&u("Warmup",this.config.warmup,Math.round(A-n),"ms",r),r}};ze=new WeakMap,Se=new WeakMap,je=new WeakMap,fe=new WeakMap,y1=new WeakMap,We=new WeakMap,w1=new WeakMap,l1=new WeakMap,d1=new WeakMap;0&&(module.exports={Human});
 //# sourceMappingURL=human.node.js.map
diff --git a/dist/human.node.js.map b/dist/human.node.js.map
index 16ba400e..8ed215cf 100644
--- a/dist/human.node.js.map
+++ b/dist/human.node.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/tfjs/tf-node.ts", "../src/human.ts", "../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts"],
-  "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      tf.enableDebugMode();\n      */\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(name: string, raw: any): void {\n  if (!raw || !raw.kernels) return;\n  const maxResults = 5;\n  const time = raw.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = raw.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n  data[name] = res;\n  log('Human profiler', name, res);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\nconst activateScore = false;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position\n          if (score > config.object.minConfidence) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            let boxRaw = [ // results normalized to range 0..1\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n              cx + (scaleBox / strideSize * boxOffset[2]),\n              cy + (scaleBox / strideSize * boxOffset[3]),\n            ];\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n  const nmsIdx = nms.dataSync();\n  tf.dispose(nms);\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    // @ts-ignore\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    resize.dispose();\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result.clone();\n      profileObject.result.dispose();\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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+  "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\nexport type { Config } from './config';\nexport type { Result } from './result';\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n/** Error message */\nexport type Error = { error: string };\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  version: string;\n  config: Config;\n  state: string;\n  image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };\n  // classes\n  tf: TensorFlow;\n  draw: {\n    drawOptions?: typeof draw.drawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  // models\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  sysinfo: { platform: string, agent: string };\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n  // definition end\n\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  // preload models, not explicitly required as it's done automatically on first use\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore\n        this.models.handpose,\n        // @ts-ignore false warning with latest @typescript-eslint\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend !== '') && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend !== '') {\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'wasm')) this.config.backend = 'tensorflow';\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          this.tf.setWasmPaths(this.config.wasmPath);\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      /* debug mode is really too mcuh\n      this.tf.enableDebugMode();\n      */\n      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n      if (this.tf.getBackend() === 'webgl') {\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        // this.tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n        // this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  // main detect function\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      process.tensor.dispose();\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      let gestureRes = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        // @ts-ignore\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    const img = this.config.warmup === 'face' ? atob(sample.face) : atob(sample.body);\n    // @ts-ignore\n    const data = tf.node.decodeJpeg(img); // tf.node is only defined when compiling for nodejs\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      // @ts-ignore\n      platform = raw[0].match(/\\(([^()]+)\\)/g)[0].replace(/\\(|\\)/g, '');\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: null,\n  gl: null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      // @ts-ignore\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      // @ts-ignore\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // @ts-ignore\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.age.modelPath);\n    if (config.debug) log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.gender.modelPath);\n    alternative = model.inputs[0].shape[3] === 1;\n    if (config.debug) log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.emotion.modelPath);\n    if (config.debug) log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.embedding.modelPath);\n    if (config.debug) log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.face.description.modelPath);\n    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 4.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    // const enhanced = tf.mul(resize, [255.0]);\n    // tf.dispose(resize);\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (mesh): { roll: number | null, yaw: number | null, pitch: number | null } => {\n  if (!mesh || mesh.length < 300) return { roll: null, yaw: null, pitch: null };\n  const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const angle = {\n    // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n    // value of 0 means center\n    // roll is face lean left/right\n    roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    // yaw is face turn left/right\n    yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n    // pitch is face move up/down\n    pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n  };\n  return angle;\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      angle: { roll: number | null, yaw: number | null, pitch: number | null },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const angle = calculateFaceAngle(face.mesh);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      angle,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: prediction.faceConfidence || prediction.boxConfidence || 0,\n        boxConfidence: prediction.boxConfidence,\n        faceConfidence: prediction.faceConfidence,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  // @ts-ignore\n  faceModels = await Promise.all([\n    (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n    (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n    (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const model = new BlazeFaceModel(blazeface, config);\n  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return model;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    // @ts-ignore\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(model) {\n    this.baseModel = model;\n    this.inputSize = model.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(config.body.modelPath);\n  const mobilenet = new modelBase.BaseModel(model);\n  if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return new PoseNet(mobilenet);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nexport async function load(config): Promise<HandPose> {\n  const [handDetectorModel, handPoseModel] = await Promise.all([\n    config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n    config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n  ]);\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  if (config.hand.enabled && config.debug) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n  if (config.hand.landmarks && config.debug) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  return [{ keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.body.modelPath);\n    if (config.debug) log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(enhance);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    enhance.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      last = parts;\n    }\n    resolve(last);\n  });\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(config.object.modelPath);\n    // @ts-ignore\n    model.inputSize = parseInt(Object.values(model.modelSignature['inputs'])[0].tensorShape.dim[2].size);\n    if (config.debug) log(`load model: ${config.object.modelPath.match(/\\/(.*)\\./)[1]}`);\n  }\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.executeAsync(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      // @ts-ignore\n      if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "// @ts-nocheck\n\nimport { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n// instance of fximage\nlet fx = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: tf.Tensor, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (input instanceof tf.Tensor) {\n    tensor = tf.clone(input);\n  } else {\n    const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n    const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) {\n      log('Human: invalid input', input);\n      return { tensor: null, canvas: null };\n    }\n    if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n        if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n        if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) {\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n      // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else {\n      // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n      // @ts-ignore\n      throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    }\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n    // @ts-ignore\n    throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n  }\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    // @ts-ignore\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      // @ts-ignore\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      // @ts-ignore\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\n\nexport const drawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = null) {\n  ctx.fillStyle = drawOptions.useDepth && z ? `rgba(${127.5 + (2 * (z || 0))}, ${127.5 - (2 * (z || 0))}, 255, 0.3)` : drawOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, drawOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height) {\n  ctx.beginPath();\n  if (drawOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = drawOptions.lineWidth;\n    ctx.moveTo(x + drawOptions.roundRect, y);\n    ctx.lineTo(x + width - drawOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + drawOptions.roundRect);\n    ctx.lineTo(x + width, y + height - drawOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - drawOptions.roundRect, y + height);\n    ctx.lineTo(x + drawOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - drawOptions.roundRect);\n    ctx.lineTo(x, y + drawOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + drawOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.fillStyle = drawOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : drawOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = []) {\n  if (points === undefined || points.length === 0) return;\n  if (!drawOptions.useCurves || points.length <= 2) {\n    lines(ctx, points);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (drawOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = drawOptions.font;\n  ctx.fillStyle = drawOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * drawOptions.lineHeight));\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * drawOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = drawOptions.font;\n    ctx.strokeStyle = drawOptions.color;\n    ctx.fillStyle = drawOptions.color;\n    if (drawOptions.drawBoxes) {\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3]);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3]);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.angle && f.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.angle.roll) / 100} yaw:${Math.trunc(100 * f.angle.yaw) / 100} pitch:${Math.trunc(100 * f.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = drawOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * drawOptions.lineHeight + f.box[1];\n      if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n        ctx.fillStyle = drawOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = drawOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (drawOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2]);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (drawOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = drawOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : drawOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (drawOptions.fillPolygons) {\n            ctx.fillStyle = drawOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : drawOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && drawOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = drawOptions.color;\n    ctx.lineWidth = drawOptions.lineWidth;\n    if (drawOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = drawOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : drawOptions.color;\n        if (drawOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1]);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y);\n        }\n      }\n    }\n    if (drawOptions.drawLabels) {\n      ctx.font = drawOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = drawOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : drawOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // torso\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 5) lines(ctx, points); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (drawOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : drawOptions.color;\n          point(ctx, pt[0], pt[1]);\n        }\n      }\n    }\n    if (drawOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = drawOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = drawOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : drawOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = drawOptions.font;\n  for (const h of result) {\n    if (drawOptions.drawBoxes) {\n      ctx.strokeStyle = drawOptions.color;\n      ctx.fillStyle = drawOptions.color;\n      if (drawOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3]);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3]);\n      if (drawOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (drawOptions.shadowColor && drawOptions.shadowColor !== '') {\n          ctx.fillStyle = drawOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = drawOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + drawOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any) {\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face);\n  body(inCanvas, result.body);\n  hand(inCanvas, result.hand);\n  gesture(inCanvas, result.gesture);\n  object(inCanvas, result.object);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  backend: string,\n  wasmPath: string,\n  debug: boolean,\n  async: boolean,\n  profile: boolean,\n  deallocate: boolean,\n  scoped: boolean,\n  videoOptimized: boolean,\n  warmup: string,\n  filter: {\n    enabled: boolean,\n    width: number,\n    height: number,\n    return: boolean,\n    brightness: number,\n    contrast: number,\n    sharpness: number,\n    blur: number\n    saturation: number,\n    hue: number,\n    negative: boolean,\n    sepia: boolean,\n    vintage: boolean,\n    kodachrome: boolean,\n    technicolor: boolean,\n    polaroid: boolean,\n    pixelate: number,\n  },\n  gesture: {\n    enabled: boolean,\n  },\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // aggresively deallocate gpu memory after each usage\n                             // only valid for webgl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // basically this skips object box boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n  filter: {\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: '../models/blazeface-back.json',\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: '../models/facemesh.json',\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: '../models/iris.json',\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: '../models/faceres.json',\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: '../models/emotion.json',\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/age.json',\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: '../models/gender.json',\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: '../models/mobileface.json',\n    },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: '../models/handdetect.json',\n    },\n    skeleton: {\n      modelPath: '../models/handskeleton.json',\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: '../models/nanodet.json',\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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   "names": []
 }
diff --git a/models/nanodet.bin b/models/nanodet.bin
index 2642818d..f040ac03 100644
Binary files a/models/nanodet.bin and b/models/nanodet.bin differ
diff --git a/models/nanodet.json b/models/nanodet.json
index 7c8a4333..34bdfccb 100644
--- a/models/nanodet.json
+++ b/models/nanodet.json
@@ -10,1066 +10,507 @@
       },
       "outputs":
       {
-          "Identity_1:0": {"name":"Identity_1:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"676"},{"size":"80"}]}},
           "Identity_2:0": {"name":"Identity_2:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"169"},{"size":"80"}]}},
-          "Identity_3:0": {"name":"Identity_3:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"2704"},{"size":"32"}]}},
-          "Identity_5:0": {"name":"Identity_5:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"169"},{"size":"32"}]}},
+          "Identity_5:0": {"name":"Identity_5:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"169"},{"size":"44"}]}},
+          "Identity_1:0": {"name":"Identity_1:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"676"},{"size":"80"}]}},
           "Identity:0": {"name":"Identity:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"2704"},{"size":"80"}]}},
-          "Identity_4:0": {"name":"Identity_4:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"676"},{"size":"32"}]}}
+          "Identity_4:0": {"name":"Identity_4:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"676"},{"size":"44"}]}},
+          "Identity_3:0": {"name":"Identity_3:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"2704"},{"size":"44"}]}}
       }
   },
   "modelTopology":
   {
       "node":
       [
-          {"name":"PartitionedCall/Where_32","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT64","tensorShape":{"dim":[{},{"size":"1"}]}}},"dtype":{"type":"DT_INT64"}}},
-          {"name":"PartitionedCall/Cast_40","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT64","tensorShape":{"dim":[{"size":"1"}]}}},"dtype":{"type":"DT_INT64"}}},
-          {"name":"PartitionedCall/GatherV2_32","op":"Const","attr":{"dtype":{"type":"DT_INT64"},"value":{"tensor":{"dtype":"DT_INT64","tensorShape":{"dim":[{}]}}}}},
-          {"name":"PartitionedCall/zeros_32","op":"Const","attr":{"dtype":{"type":"DT_INT64"},"value":{"tensor":{"dtype":"DT_INT64","tensorShape":{}}}}},
-          {"name":"PartitionedCall/onnx_tf_prefix_Transpose_301/perm","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"PartitionedCall/Where_34","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT64","tensorShape":{"dim":[{},{"size":"1"}]}}},"dtype":{"type":"DT_INT64"}}},
-          {"name":"PartitionedCall/Cast_42","op":"Const","attr":{"dtype":{"type":"DT_INT64"},"value":{"tensor":{"dtype":"DT_INT64","tensorShape":{"dim":[{"size":"1"}]}}}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_16","op":"LeakyRelu","input":["PartitionedCall/transpose_22"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_18","op":"LeakyRelu","input":["PartitionedCall/transpose_25"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
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+          {"name":"PartitionedCall/transpose_28","op":"Transpose","input":["PartitionedCall/Add_8","PartitionedCall/transpose_28/perm"],"attr":{"T":{"type":"DT_FLOAT"},"Tperm":{"type":"DT_INT32"}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_21","op":"LeakyRelu","input":["PartitionedCall/transpose_28"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
+          {"name":"PartitionedCall/Pad_9","op":"Pad","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_21","PartitionedCall/Const_18"],"attr":{"T":{"type":"DT_FLOAT"},"Tpaddings":{"type":"DT_INT32"}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_23","op":"LeakyRelu","input":["PartitionedCall/transpose_31"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_Concat_24","op":"ConcatV2","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_23","PartitionedCall/onnx_tf_prefix_LeakyRelu_21","PartitionedCall/onnx_tf_prefix_Concat_24/axis"],"attr":{"T":{"type":"DT_FLOAT"},"Tidx":{"type":"DT_INT32"},"N":{"i":"2"}}},
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+          {"name":"PartitionedCall/transpose_34","op":"Transpose","input":["PartitionedCall/Add_10","PartitionedCall/transpose_34/perm"],"attr":{"Tperm":{"type":"DT_INT32"},"T":{"type":"DT_FLOAT"}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_26","op":"LeakyRelu","input":["PartitionedCall/transpose_34"],"attr":{"alpha":{"f":0.1},"T":{"type":"DT_FLOAT"}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_Concat_27","op":"ConcatV2","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_26","PartitionedCall/onnx_tf_prefix_LeakyRelu_14","PartitionedCall/onnx_tf_prefix_Concat_27/axis"],"attr":{"T":{"type":"DT_FLOAT"},"Tidx":{"type":"DT_INT32"},"N":{"i":"2"}}},
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+          {"name":"PartitionedCall/Add_25","op":"Add","input":["PartitionedCall/convolution_25","unknown_50"],"attr":{"T":{"type":"DT_FLOAT"}}},
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+          {"name":"PartitionedCall/transpose_37","op":"Transpose","input":["PartitionedCall/Add_11","PartitionedCall/transpose_37/perm"],"attr":{"Tperm":{"type":"DT_INT32"},"T":{"type":"DT_FLOAT"}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_31","op":"LeakyRelu","input":["PartitionedCall/transpose_40"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
+          {"name":"PartitionedCall/Pad_13","op":"Pad","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_31","PartitionedCall/Const_26"],"attr":{"T":{"type":"DT_FLOAT"},"Tpaddings":{"type":"DT_INT32"}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_33","op":"LeakyRelu","input":["PartitionedCall/transpose_43"],"attr":{"alpha":{"f":0.1},"T":{"type":"DT_FLOAT"}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_Concat_34","op":"ConcatV2","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_33","PartitionedCall/onnx_tf_prefix_LeakyRelu_31","PartitionedCall/onnx_tf_prefix_Concat_34/axis"],"attr":{"Tidx":{"type":"DT_INT32"},"N":{"i":"2"},"T":{"type":"DT_FLOAT"}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_36","op":"LeakyRelu","input":["PartitionedCall/transpose_46"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
+          {"name":"PartitionedCall/Pad_15","op":"Pad","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_36","PartitionedCall/Const_30"],"attr":{"Tpaddings":{"type":"DT_INT32"},"T":{"type":"DT_FLOAT"}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_38","op":"LeakyRelu","input":["PartitionedCall/transpose_49"],"attr":{"alpha":{"f":0.1},"T":{"type":"DT_FLOAT"}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_Concat_39","op":"ConcatV2","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_38","PartitionedCall/onnx_tf_prefix_LeakyRelu_36","PartitionedCall/onnx_tf_prefix_Concat_39/axis"],"attr":{"N":{"i":"2"},"T":{"type":"DT_FLOAT"},"Tidx":{"type":"DT_INT32"}}},
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+          {"name":"PartitionedCall/onnx_tf_prefix_LeakyRelu_41","op":"LeakyRelu","input":["PartitionedCall/transpose_52"],"attr":{"T":{"type":"DT_FLOAT"},"alpha":{"f":0.1}}},
+          {"name":"PartitionedCall/onnx_tf_prefix_Concat_42","op":"ConcatV2","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_41","PartitionedCall/onnx_tf_prefix_LeakyRelu_29","PartitionedCall/onnx_tf_prefix_Concat_42/axis"],"attr":{"N":{"i":"2"},"T":{"type":"DT_FLOAT"},"Tidx":{"type":"DT_INT32"}}},
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+          {"name":"PartitionedCall/Add_26","op":"Add","input":["PartitionedCall/convolution_26","unknown_52"],"attr":{"T":{"type":"DT_FLOAT"}}},
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+          {"name":"PartitionedCall/Add_17","op":"Add","input":["PartitionedCall/convolution_17","unknown_34"],"attr":{"T":{"type":"DT_FLOAT"}}},
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+          {"name":"PartitionedCall/Pad_18","op":"Pad","input":["PartitionedCall/onnx_tf_prefix_LeakyRelu_44","PartitionedCall/Const_36"],"attr":{"T":{"type":"DT_FLOAT"},"Tpaddings":{"type":"DT_INT32"}}},
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quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_46/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_39/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_32","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_51/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_16","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_32","shape":[128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_52/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_18","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_30/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_9","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_18","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_31/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_14","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_24/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_7","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_14","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_25/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_12","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_21/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_6","shape":[3,3,64,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_12","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_22/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_19/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_16","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_27/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_8","shape":[3,3,64,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_16","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_28/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_24/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_20","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_33/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_10","shape":[3,3,64,64],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_20","shape":[64],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_34/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_6","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_12/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_3","shape":[3,3,16,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_6","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_13/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_4","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_9/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_2","shape":[3,3,32,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_4","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_10/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_9/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_8","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_15/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_4","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_8","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_16/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_1/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split","shape":[3,3,3,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_0","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_2/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/PadV2/paddings","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/PadV2/constant_values","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_3/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/transpose_4/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_2","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_6/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_1","shape":[3,3,32,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_2","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_7/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_12/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_10","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_18/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_5","shape":[3,3,64,64],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_10","shape":[64],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_19/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_27/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_22","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_36/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_11","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_22","shape":[128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_37/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_42/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/Const_34","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_54/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_17","shape":[3,3,256,256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_34","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_55/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Concat_62/axis","shape":[],"dtype":"int32"},{"name":"PartitionedCall/transpose_84/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_27","shape":[1,1,512,128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_54","shape":[128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/Cast_1","shape":[2],"dtype":"int32"},{"name":"PartitionedCall/transpose_87/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/transpose_88/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Cast_3","shape":[2],"dtype":"int32"},{"name":"PartitionedCall/transpose_89/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/transpose_90/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Cast_5","shape":[2],"dtype":"int32"},{"name":"PartitionedCall/transpose_91/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/transpose_92/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Cast_7","shape":[2],"dtype":"int32"},{"name":"PartitionedCall/transpose_93/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_65","shape":[4,2],"dtype":"int32"},{"name":"PartitionedCall/transpose_107/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_32","shape":[3,3,128,128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_70","shape":[128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_108/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/transpose_110/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/split_33","shape":[1,1,128,124],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"unknown_72","shape":[124],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"PartitionedCall/transpose_111/perm","shape":[4],"dtype":"int32"},{"name":"PartitionedCall/Const_68","shape":[2],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Split_175/split_dim","shape":[],"dtype":"int32"},{"name":"unknown_64","shape":[3],"dtype":"int32"},{"name":"PartitionedCall/Where_5","shape":[0,1],"dtype":"int32"},{"name":"PartitionedCall/Cast_13","shape":[1],"dtype":"int32"},{"name":"PartitionedCall/GatherV2_5","shape":[0],"dtype":"int32"},{"name":"PartitionedCall/zeros_5","shape":[],"dtype":"int32"},{"name":"PartitionedCall/onnx_tf_prefix_Transpose_182/perm","shape":[3],"dtype":"int32"}]
       }
   ]
 }
\ No newline at end of file
diff --git a/package.json b/package.json
index 0e483721..15978773 100644
--- a/package.json
+++ b/package.json
@@ -56,13 +56,13 @@
     "@tensorflow/tfjs-layers": "^3.3.0",
     "@tensorflow/tfjs-node": "^3.3.0",
     "@tensorflow/tfjs-node-gpu": "^3.3.0",
-    "@types/node": "^14.14.36",
+    "@types/node": "^14.14.37",
     "@typescript-eslint/eslint-plugin": "^4.19.0",
     "@typescript-eslint/parser": "^4.19.0",
     "@vladmandic/pilogger": "^0.2.15",
     "chokidar": "^3.5.1",
     "dayjs": "^1.10.4",
-    "esbuild": "^0.10.1",
+    "esbuild": "^0.10.2",
     "eslint": "^7.23.0",
     "eslint-config-airbnb-base": "^14.2.1",
     "eslint-plugin-import": "^2.22.1",
diff --git a/src/faceres/faceres.ts b/src/faceres/faceres.ts
index 4b6c83a6..a5700fda 100644
--- a/src/faceres/faceres.ts
+++ b/src/faceres/faceres.ts
@@ -110,10 +110,9 @@ export async function predict(image, config) {
     if (!config.profile) {
       if (config.face.description.enabled) resT = await model.predict(enhanced);
     } else {
-      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};
-      resT = profileAge.result.clone();
-      profileAge.result.dispose();
-      profile.run('age', profileAge);
+      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};
+      resT = profileDesc.result;
+      profile.run('faceres', profileDesc);
     }
     tf.dispose(enhanced);
 
diff --git a/src/human.ts b/src/human.ts
index 9a914b20..de5c3b10 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -292,8 +292,10 @@ export class Human {
       }
       this.tf.enableProdMode();
       /* debug mode is really too mcuh
-      tf.enableDebugMode();
+      this.tf.enableDebugMode();
       */
+      this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);
+      this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);
       if (this.tf.getBackend() === 'webgl') {
         if (this.config.deallocate) {
           log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);
diff --git a/src/nanodet/nanodet.ts b/src/nanodet/nanodet.ts
index 5ffffd52..5529bd4e 100644
--- a/src/nanodet/nanodet.ts
+++ b/src/nanodet/nanodet.ts
@@ -8,7 +8,6 @@ let last: Array<{}> = [];
 let skipped = Number.MAX_SAFE_INTEGER;
 
 const scaleBox = 2.5; // increase box size
-const activateScore = false;
 
 export async function load(config) {
   if (!model) {
@@ -28,24 +27,27 @@ async function process(res, inputSize, outputShape, config) {
     tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors
       const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704
       // find boxes and scores output depending on stride
-      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === 80))?.squeeze();
-      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < 80))?.squeeze();
+      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();
+      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride
       const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself
-      const scores = activateScore ? scoresT.exp(1).arraySync() : scoresT.arraySync(); // optionally use exponential scores or just as-is
+      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is
       for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)
         for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class
-          const score = scores[i][j] - (activateScore ? 1 : 0); // get score for current position
-          if (score > config.object.minConfidence) {
+          const score = scores[i][j]; // get score for current position
+          if (score > config.object.minConfidence && j !== 61) {
             const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1
             const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1
             const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores
-            let boxRaw = [ // results normalized to range 0..1
+            const [x, y] = [
               cx - (scaleBox / strideSize * boxOffset[0]),
               cy - (scaleBox / strideSize * boxOffset[1]),
-              cx + (scaleBox / strideSize * boxOffset[2]),
-              cy + (scaleBox / strideSize * boxOffset[3]),
             ];
+            const [w, h] = [
+              cx + (scaleBox / strideSize * boxOffset[2]) - x,
+              cy + (scaleBox / strideSize * boxOffset[3]) - y,
+            ];
+            let boxRaw = [x, y, w, h]; // results normalized to range 0..1
             boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords
             const box = [ // results normalized to input image pixels
               boxRaw[0] * outputShape[0],
@@ -77,14 +79,16 @@ async function process(res, inputSize, outputShape, config) {
   // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)
   const nmsBoxes = results.map((a) => a.boxRaw);
   const nmsScores = results.map((a) => a.score);
-  const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);
-  const nmsIdx = nms.dataSync();
-  tf.dispose(nms);
+  let nmsIdx: any[] = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);
+    nmsIdx = nms.dataSync();
+    tf.dispose(nms);
+  }
 
   // filter & sort results
   results = results
     .filter((a, idx) => nmsIdx.includes(idx))
-    // @ts-ignore
     .sort((a, b) => (b.score - a.score));
 
   return results;
@@ -103,17 +107,16 @@ export async function predict(image, config) {
     const outputSize = [image.shape[2], image.shape[1]];
     const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);
     const norm = resize.div(255);
-    resize.dispose();
     const transpose = norm.transpose([0, 3, 1, 2]);
     norm.dispose();
+    resize.dispose();
 
     let objectT;
     if (!config.profile) {
-      if (config.object.enabled) objectT = await model.predict(transpose);
+      if (config.object.enabled) objectT = await model.executeAsync(transpose);
     } else {
-      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};
-      objectT = profileObject.result.clone();
-      profileObject.result.dispose();
+      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};
+      objectT = profileObject.result;
       profile.run('object', profileObject);
     }
     transpose.dispose();
diff --git a/src/profile.ts b/src/profile.ts
index 43080f30..cd9101fa 100644
--- a/src/profile.ts
+++ b/src/profile.ts
@@ -2,23 +2,31 @@ import { log } from './helpers';
 
 export const data = {};
 
-export function run(name: string, raw: any): void {
-  if (!raw || !raw.kernels) return;
+export function run(modelName: string, profileData: any): void {
+  if (!profileData || !profileData.kernels) return;
   const maxResults = 5;
-  const time = raw.kernels
+  const time = profileData.kernels
     .filter((a) => a.kernelTimeMs > 0)
     .reduce((a, b) => a += b.kernelTimeMs, 0);
-  const slowest = raw.kernels
+  const slowest = profileData.kernels
     .map((a, i) => { a.id = i; return a; })
     .filter((a) => a.kernelTimeMs > 0)
     .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);
-  const largest = raw.kernels
+  const largest = profileData.kernels
     .map((a, i) => { a.id = i; return a; })
     .filter((a) => a.totalBytesSnapshot > 0)
     .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);
   if (slowest.length > maxResults) slowest.length = maxResults;
   if (largest.length > maxResults) largest.length = maxResults;
-  const res = { newBytes: raw.newBytes, newTensors: raw.newTensors, peakBytes: raw.peakBytes, numKernelOps: raw.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };
-  data[name] = res;
-  log('Human profiler', name, res);
+  data[modelName] = {
+    model: modelName,
+    newBytes: profileData.newBytes,
+    newTensors: profileData.newTensors,
+    peakBytes: profileData.peakBytes,
+    numKernelOps: profileData.kernels.length,
+    timeKernelOps: time,
+    slowestKernelOps: slowest,
+    largestKernelOps: largest,
+  };
+  log('profiler', modelName, data[modelName]);
 }
diff --git a/types/profile.d.ts b/types/profile.d.ts
index 8bbb25bb..323c7511 100644
--- a/types/profile.d.ts
+++ b/types/profile.d.ts
@@ -1,2 +1,2 @@
 export declare const data: {};
-export declare function run(name: string, raw: any): void;
+export declare function run(modelName: string, profileData: any): void;